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opencv/modules/contrib/include/opencv2/contrib.hpp

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#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<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 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;
};
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 };
};
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();
}
#include "opencv2/contrib/openfabmap.hpp"
#endif
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