/*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. // 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_CONTRIB_HPP__ #define __OPENCV_CONTRIB_HPP__ #include "opencv2/core.hpp" #include "opencv2/imgproc.hpp" #include "opencv2/features2d.hpp" #include "opencv2/objdetect.hpp" 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& points, int maxLevels = 10, int minPoints = 20 ); virtual ~Octree(); virtual void buildTree( const std::vector& points, int maxLevels = 10, int minPoints = 20 ); virtual void getPointsWithinSphere( const Point3f& center, float radius, std::vector& points ) const; const std::vector& getNodes() const { return nodes; } private: int minPoints; std::vector points; std::vector nodes; virtual void buildNext(size_t node_ind); }; class CV_EXPORTS Mesh3D { public: struct EmptyMeshException {}; Mesh3D(); Mesh3D(const std::vector& vtx); ~Mesh3D(); void buildOctree(); void clearOctree(); float estimateResolution(float tryRatio = 0.1f); void computeNormals(float normalRadius, int minNeighbors = 20); void computeNormals(const std::vector& subset, float normalRadius, int minNeighbors = 20); void writeAsVrml(const String& file, const std::vector& colors = std::vector()) const; std::vector vtx; std::vector 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 selectRandomSubset(float ratio); void setSubset(const std::vector& subset); void compute(); void match(const SpinImageModel& scene, std::vector< std::vector >& 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& indeces, std::vector& corrCoeffs, bool useExtremeOutliers = true) const; void repackSpinImages(const std::vector& mask, Mat& spinImages, bool reAlloc = true) const; std::vector subset; Mesh3D mesh; Mat spinImages; }; 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& descriptors, Size winStride=Size(), const std::vector& locations=std::vector()) 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 }; }; CV_EXPORTS_W int chamerMatching( Mat& img, Mat& templ, CV_OUT std::vector >& results, CV_OUT std::vector& 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 GetListFiles ( const String& path, const String & exten = "*", bool addPath = true ); static std::vector GetListFilesR ( const String& path, const String & exten = "*", bool addPath = true ); static std::vector 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& 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& iterCounts=std::vector(), const std::vector& minGradientMagnitudes=std::vector(), 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 Rsr; std::vector Csr; std::vector Wsr; int S, R, M, N, ind1; int top, bottom,left,right; double ro0, romax, a, q; struct kernel { kernel() { w = 0; } std::vector weights; int w; }; Mat ETAyx; Mat CSIyx; std::vector 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 > L; std::vector 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 createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); CV_EXPORTS_W Ptr createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX); CV_EXPORTS_W Ptr 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(); } #include "opencv2/contrib/openfabmap.hpp" #endif