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13523fd53a
opencv/modules/features2d/include/opencv2/features2d/features2d.hpp
Victor Erukhimov 13523fd53a Fixing compiler errors for outlet_pose_estimation
2010-05-26 12:34:48 +00:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
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// Third party copyrights are property of their respective owners.
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// are permitted provided that the following conditions are met:
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// this list of conditions and the following disclaimer.
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// and/or other materials provided with the distribution.
//
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#ifndef __OPENCV_FEATURES_2D_HPP__
#define __OPENCV_FEATURES_2D_HPP__
#include "opencv2/core/core.hpp"
#ifdef __cplusplus
#include <limits>
extern "C" {
#endif
typedef struct CvSURFPoint
{
CvPoint2D32f pt;
int laplacian;
int size;
float dir;
float hessian;
} CvSURFPoint;
CV_INLINE CvSURFPoint cvSURFPoint( CvPoint2D32f pt, int laplacian,
int size, float dir CV_DEFAULT(0),
float hessian CV_DEFAULT(0))
{
CvSURFPoint kp;
kp.pt = pt;
kp.laplacian = laplacian;
kp.size = size;
kp.dir = dir;
kp.hessian = hessian;
return kp;
}
typedef struct CvSURFParams
{
int extended;
double hessianThreshold;
int nOctaves;
int nOctaveLayers;
} CvSURFParams;
CVAPI(CvSURFParams) cvSURFParams( double hessianThreshold, int extended CV_DEFAULT(0) );
// If useProvidedKeyPts!=0, keypoints are not detected, but descriptors are computed
// at the locations provided in keypoints (a CvSeq of CvSURFPoint).
CVAPI(void) cvExtractSURF( const CvArr* img, const CvArr* mask,
CvSeq** keypoints, CvSeq** descriptors,
CvMemStorage* storage, CvSURFParams params, int useProvidedKeyPts CV_DEFAULT(0) );
typedef struct CvMSERParams
{
// delta, in the code, it compares (size_{i}-size_{i-delta})/size_{i-delta}
int delta;
// prune the area which bigger/smaller than max_area/min_area
int maxArea;
int minArea;
// prune the area have simliar size to its children
float maxVariation;
// trace back to cut off mser with diversity < min_diversity
float minDiversity;
/* the next few params for MSER of color image */
// for color image, the evolution steps
int maxEvolution;
// the area threshold to cause re-initialize
double areaThreshold;
// ignore too small margin
double minMargin;
// the aperture size for edge blur
int edgeBlurSize;
} CvMSERParams;
CVAPI(CvMSERParams) cvMSERParams( int delta CV_DEFAULT(5), int min_area CV_DEFAULT(60),
int max_area CV_DEFAULT(14400), float max_variation CV_DEFAULT(.25f),
float min_diversity CV_DEFAULT(.2f), int max_evolution CV_DEFAULT(200),
double area_threshold CV_DEFAULT(1.01),
double min_margin CV_DEFAULT(.003),
int edge_blur_size CV_DEFAULT(5) );
// Extracts the contours of Maximally Stable Extremal Regions
CVAPI(void) cvExtractMSER( CvArr* _img, CvArr* _mask, CvSeq** contours, CvMemStorage* storage, CvMSERParams params );
typedef struct CvStarKeypoint
{
CvPoint pt;
int size;
float response;
} CvStarKeypoint;
CV_INLINE CvStarKeypoint cvStarKeypoint(CvPoint pt, int size, float response)
{
CvStarKeypoint kpt;
kpt.pt = pt;
kpt.size = size;
kpt.response = response;
return kpt;
}
typedef struct CvStarDetectorParams
{
int maxSize;
int responseThreshold;
int lineThresholdProjected;
int lineThresholdBinarized;
int suppressNonmaxSize;
} CvStarDetectorParams;
CV_INLINE CvStarDetectorParams cvStarDetectorParams(
int maxSize CV_DEFAULT(45),
int responseThreshold CV_DEFAULT(30),
int lineThresholdProjected CV_DEFAULT(10),
int lineThresholdBinarized CV_DEFAULT(8),
int suppressNonmaxSize CV_DEFAULT(5))
{
CvStarDetectorParams params;
params.maxSize = maxSize;
params.responseThreshold = responseThreshold;
params.lineThresholdProjected = lineThresholdProjected;
params.lineThresholdBinarized = lineThresholdBinarized;
params.suppressNonmaxSize = suppressNonmaxSize;
return params;
}
CVAPI(CvSeq*) cvGetStarKeypoints( const CvArr* img, CvMemStorage* storage,
CvStarDetectorParams params CV_DEFAULT(cvStarDetectorParams()));
#ifdef __cplusplus
}
namespace cv
{
// CvAffinePose: defines a parameterized affine transformation of an image patch.
// An image patch is rotated on angle phi (in degrees), then scaled lambda1 times
// along horizontal and lambda2 times along vertical direction, and then rotated again
// on angle (theta - phi).
class CV_EXPORTS CvAffinePose
{
public:
float phi;
float theta;
float lambda1;
float lambda2;
};
class CV_EXPORTS KeyPoint
{
public:
KeyPoint() : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
KeyPoint(Point2f _pt, float _size, float _angle=-1,
float _response=0, int _octave=0, int _class_id=-1)
: pt(_pt), size(_size), angle(_angle),
response(_response), octave(_octave), class_id(_class_id) {}
KeyPoint(float x, float y, float _size, float _angle=-1,
float _response=0, int _octave=0, int _class_id=-1)
: pt(x, y), size(_size), angle(_angle),
response(_response), octave(_octave), class_id(_class_id) {}
static void convert(const std::vector<KeyPoint>& u, std::vector<Point2f>& v);
static void convert(const std::vector<Point2f>& u, std::vector<KeyPoint>& v,
float size=1, float response=1, int octave=0, int class_id=-1);
Point2f pt;
float size;
float angle;
float response;
int octave;
int class_id;
};
CV_EXPORTS void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
CV_EXPORTS void read(const FileNode& node, vector<KeyPoint>& keypoints);
class CV_EXPORTS SIFT
{
public:
struct CommonParams
{
static const int DEFAULT_NOCTAVES = 4;
static const int DEFAULT_NOCTAVE_LAYERS = 3;
static const int DEFAULT_FIRST_OCTAVE = -1;
enum{ FIRST_ANGLE = 0, AVERAGE_ANGLE = 1 };
CommonParams() : nOctaves(DEFAULT_NOCTAVES), nOctaveLayers(DEFAULT_NOCTAVE_LAYERS),
firstOctave(DEFAULT_FIRST_OCTAVE), angleMode(FIRST_ANGLE) {}
CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave, int _angleMode ) :
nOctaves(_nOctaves), nOctaveLayers(_nOctaveLayers),
firstOctave(_firstOctave), angleMode(_angleMode) {}
int nOctaves, nOctaveLayers, firstOctave;
int angleMode;
};
struct DetectorParams
{
static double GET_DEFAULT_THRESHOLD() { return 0.04 / SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS / 2.0; }
static double GET_DEFAULT_EDGE_THRESHOLD() { return 10.0; }
DetectorParams() : threshold(GET_DEFAULT_THRESHOLD()), edgeThreshold(GET_DEFAULT_EDGE_THRESHOLD()) {}
DetectorParams( double _threshold, double _edgeThreshold ) :
threshold(_threshold), edgeThreshold(_edgeThreshold) {}
double threshold, edgeThreshold;
};
struct DescriptorParams
{
static double GET_DEFAULT_MAGNIFICATION() { return 3.0; }
static const bool DEFAULT_IS_NORMALIZE = true;
static const int DESCRIPTOR_SIZE = 128;
DescriptorParams() : magnification(GET_DEFAULT_MAGNIFICATION()), isNormalize(DEFAULT_IS_NORMALIZE),
recalculateAngles(true) {}
DescriptorParams( double _magnification, bool _isNormalize, bool _recalculateAngles ) :
magnification(_magnification), isNormalize(_isNormalize),
recalculateAngles(_recalculateAngles) {}
double magnification;
bool isNormalize;
bool recalculateAngles;
};
SIFT();
// sift-detector constructor
SIFT( double _threshold, double _edgeThreshold,
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
int _angleMode=CommonParams::FIRST_ANGLE );
// sift-descriptor constructor
SIFT( double _magnification, bool _isNormalize=true,
bool _recalculateAngles = true,
int _nOctaves=CommonParams::DEFAULT_NOCTAVES,
int _nOctaveLayers=CommonParams::DEFAULT_NOCTAVE_LAYERS,
int _firstOctave=CommonParams::DEFAULT_FIRST_OCTAVE,
int _angleMode=CommonParams::FIRST_ANGLE );
SIFT( const CommonParams& _commParams,
const DetectorParams& _detectorParams = DetectorParams(),
const DescriptorParams& _descriptorParams = DescriptorParams() );
int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints) const;
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints,
Mat& descriptors,
bool useProvidedKeypoints=false) const;
protected:
CommonParams commParams;
DetectorParams detectorParams;
DescriptorParams descriptorParams;
};
class CV_EXPORTS SURF : public CvSURFParams
{
public:
SURF();
SURF(double _hessianThreshold, int _nOctaves=4,
int _nOctaveLayers=2, bool _extended=false);
int descriptorSize() const;
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints) const;
void operator()(const Mat& img, const Mat& mask,
vector<KeyPoint>& keypoints,
vector<float>& descriptors,
bool useProvidedKeypoints=false) const;
};
class CV_EXPORTS MSER : public CvMSERParams
{
public:
MSER();
MSER( int _delta, int _min_area, int _max_area,
float _max_variation, float _min_diversity,
int _max_evolution, double _area_threshold,
double _min_margin, int _edge_blur_size );
void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
};
class CV_EXPORTS StarDetector : public CvStarDetectorParams
{
public:
StarDetector();
StarDetector(int _maxSize, int _responseThreshold,
int _lineThresholdProjected,
int _lineThresholdBinarized,
int _suppressNonmaxSize);
void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
};
// detect corners using FAST algorithm
CV_EXPORTS void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmax_supression=true );
class CV_EXPORTS PatchGenerator
{
public:
PatchGenerator();
PatchGenerator(double _backgroundMin, double _backgroundMax,
double _noiseRange, bool _randomBlur=true,
double _lambdaMin=0.6, double _lambdaMax=1.5,
double _thetaMin=-CV_PI, double _thetaMax=CV_PI,
double _phiMin=-CV_PI, double _phiMax=CV_PI );
void operator()(const Mat& image, Point2f pt, Mat& patch, Size patchSize, RNG& rng) const;
void operator()(const Mat& image, const Mat& transform, Mat& patch,
Size patchSize, RNG& rng) const;
void warpWholeImage(const Mat& image, Mat& matT, Mat& buf,
Mat& warped, int border, RNG& rng) const;
void generateRandomTransform(Point2f srcCenter, Point2f dstCenter,
Mat& transform, RNG& rng, bool inverse=false) const;
void setAffineParam(double lambda, double theta, double phi);
double backgroundMin, backgroundMax;
double noiseRange;
bool randomBlur;
double lambdaMin, lambdaMax;
double thetaMin, thetaMax;
double phiMin, phiMax;
};
class CV_EXPORTS LDetector
{
public:
LDetector();
LDetector(int _radius, int _threshold, int _nOctaves,
int _nViews, double _baseFeatureSize, double _clusteringDistance);
void operator()(const Mat& image, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
void operator()(const vector<Mat>& pyr, vector<KeyPoint>& keypoints, int maxCount=0, bool scaleCoords=true) const;
void getMostStable2D(const Mat& image, vector<KeyPoint>& keypoints,
int maxCount, const PatchGenerator& patchGenerator) const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
int radius;
int threshold;
int nOctaves;
int nViews;
bool verbose;
double baseFeatureSize;
double clusteringDistance;
};
typedef LDetector YAPE;
class CV_EXPORTS FernClassifier
{
public:
FernClassifier();
FernClassifier(const FileNode& node);
FernClassifier(const vector<Point2f>& points,
const vector<Ptr<Mat> >& refimgs,
const vector<int>& labels=vector<int>(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~FernClassifier();
virtual void read(const FileNode& n);
virtual void write(FileStorage& fs, const String& name=String()) const;
virtual void trainFromSingleView(const Mat& image,
const vector<KeyPoint>& keypoints,
int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<Point2f>& points,
const vector<Ptr<Mat> >& refimgs,
const vector<int>& labels=vector<int>(),
int _nclasses=0, int _patchSize=PATCH_SIZE,
int _signatureSize=DEFAULT_SIGNATURE_SIZE,
int _nstructs=DEFAULT_STRUCTS,
int _structSize=DEFAULT_STRUCT_SIZE,
int _nviews=DEFAULT_VIEWS,
int _compressionMethod=COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator());
virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
virtual int operator()(const Mat& patch, vector<float>& signature) const;
virtual void clear();
void setVerbose(bool verbose);
int getClassCount() const;
int getStructCount() const;
int getStructSize() const;
int getSignatureSize() const;
int getCompressionMethod() const;
Size getPatchSize() const;
struct Feature
{
uchar x1, y1, x2, y2;
Feature() : x1(0), y1(0), x2(0), y2(0) {}
Feature(int _x1, int _y1, int _x2, int _y2)
: x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
{}
template<typename _Tp> bool operator ()(const Mat_<_Tp>& patch) const
{ return patch(y1,x1) > patch(y2, x2); }
};
enum
{
PATCH_SIZE = 31,
DEFAULT_STRUCTS = 50,
DEFAULT_STRUCT_SIZE = 9,
DEFAULT_VIEWS = 5000,
DEFAULT_SIGNATURE_SIZE = 176,
COMPRESSION_NONE = 0,
COMPRESSION_RANDOM_PROJ = 1,
COMPRESSION_PCA = 2,
DEFAULT_COMPRESSION_METHOD = COMPRESSION_NONE
};
protected:
virtual void prepare(int _nclasses, int _patchSize, int _signatureSize,
int _nstructs, int _structSize,
int _nviews, int _compressionMethod);
virtual void finalize(RNG& rng);
virtual int getLeaf(int fidx, const Mat& patch) const;
bool verbose;
int nstructs;
int structSize;
int nclasses;
int signatureSize;
int compressionMethod;
int leavesPerStruct;
Size patchSize;
vector<Feature> features;
vector<int> classCounters;
vector<float> posteriors;
};
class CV_EXPORTS PlanarObjectDetector
{
public:
PlanarObjectDetector();
PlanarObjectDetector(const FileNode& node);
PlanarObjectDetector(const vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual ~PlanarObjectDetector();
virtual void train(const vector<Mat>& pyr, int _npoints=300,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
virtual void train(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
int _patchSize=FernClassifier::PATCH_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
const LDetector& detector=LDetector(),
const PatchGenerator& patchGenerator=PatchGenerator());
Rect getModelROI() const;
vector<KeyPoint> getModelPoints() const;
const LDetector& getDetector() const;
const FernClassifier& getClassifier() const;
void setVerbose(bool verbose);
void read(const FileNode& node);
void write(FileStorage& fs, const String& name=String()) const;
bool operator()(const Mat& image, Mat& H, vector<Point2f>& corners) const;
bool operator()(const vector<Mat>& pyr, const vector<KeyPoint>& keypoints,
Mat& H, vector<Point2f>& corners, vector<int>* pairs=0) const;
protected:
bool verbose;
Rect modelROI;
vector<KeyPoint> modelPoints;
LDetector ldetector;
FernClassifier fernClassifier;
};
/****************************************************************************************\
* Calonder Descriptor *
\****************************************************************************************/
struct CV_EXPORTS DefaultRngAuto
{
const static uint64 def_state = (uint64)-1;
const uint64 old_state;
DefaultRngAuto() : old_state(theRNG().state) { theRNG().state = def_state; }
~DefaultRngAuto() { theRNG().state = old_state; }
DefaultRngAuto& operator=(const DefaultRngAuto&);
};
/*
A pseudo-random number generator usable with std::random_shuffle.
*/
typedef cv::RNG CalonderRng;
typedef unsigned int int_type;
//----------------------------
//randomized_tree.h
//class RTTester;
//namespace features {
static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
static const float LOWER_QUANT_PERC = .03f;
static const float UPPER_QUANT_PERC = .92f;
static const int PATCH_SIZE = 32;
static const int DEFAULT_DEPTH = 9;
static const int DEFAULT_VIEWS = 5000;
struct RTreeNode;
struct BaseKeypoint
{
int x;
int y;
IplImage* image;
BaseKeypoint()
: x(0), y(0), image(NULL)
{}
BaseKeypoint(int x, int y, IplImage* image)
: x(x), y(y), image(image)
{}
};
class CSMatrixGenerator {
public:
typedef enum { PDT_GAUSS=1, PDT_BERNOULLI, PDT_DBFRIENDLY } PHI_DISTR_TYPE;
~CSMatrixGenerator();
static float* getCSMatrix(int m, int n, PHI_DISTR_TYPE dt); // do NOT free returned pointer
private:
static float *cs_phi_; // matrix for compressive sensing
static int cs_phi_m_, cs_phi_n_;
};
template< typename T >
struct AlignedMemBlock
{
AlignedMemBlock() : raw(NULL), data(NULL) { };
// Alloc's an `a` bytes-aligned block good to hold `sz` elements of class T
AlignedMemBlock(const int n, const int a)
{
alloc(n, a);
}
~AlignedMemBlock()
{
free(raw);
}
void alloc(const int n, const int a)
{
uchar* raw = (uchar*)malloc(n*sizeof(T) + a);
int delta = (a - uint64(raw)%a)%a; // # bytes required for padding s.t. we get `a`-aligned
data = reinterpret_cast<T*>(raw + delta);
}
// Methods to access the aligned data. NEVER EVER FREE A RETURNED POINTER!
inline T* p() { return data; }
inline T* operator()() { return data; }
private:
T *raw; // raw block, probably not aligned
T *data; // exposed data, aligned, DO NOT FREE
};
typedef AlignedMemBlock<float> FloatSignature;
typedef AlignedMemBlock<uchar> Signature;
class CV_EXPORTS RandomizedTree
{
public:
friend class RTreeClassifier;
//friend class ::RTTester;
RandomizedTree();
~RandomizedTree();
void train(std::vector<BaseKeypoint> const& base_set, cv::RNG &rng,
int depth, int views, size_t reduced_num_dim, int num_quant_bits);
void train(std::vector<BaseKeypoint> const& base_set, cv::RNG &rng,
PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim,
int num_quant_bits);
// following two funcs are EXPERIMENTAL (do not use unless you know exactly what you do)
static void quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode=0);
static void quantizeVector(float *src, int dim, int N, float bnds[2], uchar *dst);
// patch_data must be a 32x32 array (no row padding)
float* getPosterior(uchar* patch_data);
const float* getPosterior(uchar* patch_data) const;
uchar* getPosterior2(uchar* patch_data);
void read(const char* file_name, int num_quant_bits);
void read(std::istream &is, int num_quant_bits);
void write(const char* file_name) const;
void write(std::ostream &os) const;
inline int classes() { return classes_; }
inline int depth() { return depth_; }
inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); }
// debug
void savePosteriors(std::string url, bool append=false);
void savePosteriors2(std::string url, bool append=false);
private:
int classes_;
int depth_;
int num_leaves_;
std::vector<RTreeNode> nodes_;
//float **posteriors_; // 16-bytes aligned posteriors
//uchar **posteriors2_; // 16-bytes aligned posteriors
FloatSignature *posteriors_;
Signature *posteriors2_;
std::vector<int> leaf_counts_;
void createNodes(int num_nodes, cv::RNG &rng);
void allocPosteriorsAligned(int num_leaves, int num_classes);
void freePosteriors(int which); // which: 1=posteriors_, 2=posteriors2_, 3=both
void init(int classes, int depth, cv::RNG &rng);
void addExample(int class_id, uchar* patch_data);
void finalize(size_t reduced_num_dim, int num_quant_bits);
int getIndex(uchar* patch_data) const;
inline float* getPosteriorByIndex(int index);
inline uchar* getPosteriorByIndex2(int index);
inline const float* getPosteriorByIndex(int index) const;
//void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
void convertPosteriorsToChar();
void makePosteriors2(int num_quant_bits);
void compressLeaves(size_t reduced_num_dim);
void estimateQuantPercForPosteriors(float perc[2]);
};
struct RTreeNode
{
short offset1, offset2;
RTreeNode() {}
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
: offset1(y1*PATCH_SIZE + x1),
offset2(y2*PATCH_SIZE + x2)
{}
//! Left child on 0, right child on 1
inline bool operator() (uchar* patch_data) const
{
return patch_data[offset1] > patch_data[offset2];
}
};
//} // namespace features
//----------------------------
//rtree_classifier.h
//class RTTester;
//namespace features {
class CV_EXPORTS RTreeClassifier
{
public:
//friend class ::RTTester;
static const int DEFAULT_TREES = 80;
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
//static const int SIG_LEN = 176;
RTreeClassifier();
//modified
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng,
int num_trees = RTreeClassifier::DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
int views = DEFAULT_VIEWS,
size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
bool print_status = true);
void train(std::vector<BaseKeypoint> const& base_set,
cv::RNG &rng,
PatchGenerator &make_patch,
int num_trees = DEFAULT_TREES,
int depth = DEFAULT_DEPTH,
int views = DEFAULT_VIEWS,
size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
bool print_status = true);
// sig must point to a memory block of at least classes()*sizeof(float|uchar) bytes
void getSignature(IplImage *patch, uchar *sig);
void getSignature(IplImage *patch, float *sig);
void getSparseSignature(IplImage *patch, float *sig, float thresh);
// TODO: deprecated in favor of getSignature overload, remove
void getFloatSignature(IplImage *patch, float *sig) { getSignature(patch, sig); }
static int countNonZeroElements(float *vec, int n, double tol=1e-10);
static inline void safeSignatureAlloc(uchar **sig, int num_sig=1, int sig_len=176);
static inline uchar* safeSignatureAlloc(int num_sig=1, int sig_len=176);
inline int classes() const { return classes_; }
inline int original_num_classes() { return original_num_classes_; }
void setQuantization(int num_quant_bits);
void discardFloatPosteriors();
void read(const char* file_name);
void read(std::istream &is);
void write(const char* file_name) const;
void write(std::ostream &os) const;
// experimental and debug
void saveAllFloatPosteriors(std::string file_url);
void saveAllBytePosteriors(std::string file_url);
void setFloatPosteriorsFromTextfile_176(std::string url);
float countZeroElements();
std::vector<RandomizedTree> trees_;
private:
int classes_;
int num_quant_bits_;
uchar **posteriors_;
ushort *ptemp_;
int original_num_classes_;
bool keep_floats_;
};
/****************************************************************************************\
* One-Way Descriptor *
\****************************************************************************************/
class CV_EXPORTS OneWayDescriptor
{
public:
OneWayDescriptor();
~OneWayDescriptor();
// allocates memory for given descriptor parameters
void Allocate(int pose_count, CvSize size, int nChannels);
// GenerateSamples: generates affine transformed patches with averaging them over small transformation variations.
// If external poses and transforms were specified, uses them instead of generating random ones
// - pose_count: the number of poses to be generated
// - frontal: the input patch (can be a roi in a larger image)
// - norm: if nonzero, normalizes the output patch so that the sum of pixel intensities is 1
void GenerateSamples(int pose_count, IplImage* frontal, int norm = 0);
// GenerateSamplesFast: generates affine transformed patches with averaging them over small transformation variations.
// Uses precalculated transformed pca components.
// - frontal: the input patch (can be a roi in a larger image)
// - pca_hr_avg: pca average vector
// - pca_hr_eigenvectors: pca eigenvectors
// - pca_descriptors: an array of precomputed descriptors of pca components containing their affine transformations
// pca_descriptors[0] corresponds to the average, pca_descriptors[1]-pca_descriptors[pca_dim] correspond to eigenvectors
void GenerateSamplesFast(IplImage* frontal, CvMat* pca_hr_avg,
CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);
// sets the poses and corresponding transforms
void SetTransforms(CvAffinePose* poses, CvMat** transforms);
// Initialize: builds a descriptor.
// - pose_count: the number of poses to build. If poses were set externally, uses them rather than generating random ones
// - frontal: input patch. Can be a roi in a larger image
// - feature_name: the feature name to be associated with the descriptor
// - norm: if 1, the affine transformed patches are normalized so that their sum is 1
void Initialize(int pose_count, IplImage* frontal, const char* feature_name = 0, int norm = 0);
// InitializeFast: builds a descriptor using precomputed descriptors of pca components
// - pose_count: the number of poses to build
// - frontal: input patch. Can be a roi in a larger image
// - feature_name: the feature name to be associated with the descriptor
// - pca_hr_avg: average vector for PCA
// - pca_hr_eigenvectors: PCA eigenvectors (one vector per row)
// - pca_descriptors: precomputed descriptors of PCA components, the first descriptor for the average vector
// followed by the descriptors for eigenvectors
void InitializeFast(int pose_count, IplImage* frontal, const char* feature_name,
CvMat* pca_hr_avg, CvMat* pca_hr_eigenvectors, OneWayDescriptor* pca_descriptors);
// ProjectPCASample: unwarps an image patch into a vector and projects it into PCA space
// - patch: input image patch
// - avg: PCA average vector
// - eigenvectors: PCA eigenvectors, one per row
// - pca_coeffs: output PCA coefficients
void ProjectPCASample(IplImage* patch, CvMat* avg, CvMat* eigenvectors, CvMat* pca_coeffs) const;
// InitializePCACoeffs: projects all warped patches into PCA space
// - avg: PCA average vector
// - eigenvectors: PCA eigenvectors, one per row
void InitializePCACoeffs(CvMat* avg, CvMat* eigenvectors);
// EstimatePose: finds the closest match between an input patch and a set of patches with different poses
// - patch: input image patch
// - pose_idx: the output index of the closest pose
// - distance: the distance to the closest pose (L2 distance)
void EstimatePose(IplImage* patch, int& pose_idx, float& distance) const;
// EstimatePosePCA: finds the closest match between an input patch and a set of patches with different poses.
// The distance between patches is computed in PCA space
// - patch: input image patch
// - pose_idx: the output index of the closest pose
// - distance: distance to the closest pose (L2 distance in PCA space)
// - avg: PCA average vector. If 0, matching without PCA is used
// - eigenvectors: PCA eigenvectors, one per row
void EstimatePosePCA(CvArr* patch, int& pose_idx, float& distance, CvMat* avg, CvMat* eigenvalues) const;
// GetPatchSize: returns the size of each image patch after warping (2 times smaller than the input patch)
CvSize GetPatchSize() const
{
return m_patch_size;
}
// GetInputPatchSize: returns the required size of the patch that the descriptor is built from
// (2 time larger than the patch after warping)
CvSize GetInputPatchSize() const
{
return cvSize(m_patch_size.width*2, m_patch_size.height*2);
}
// GetPatch: returns a patch corresponding to specified pose index
// - index: pose index
// - return value: the patch corresponding to specified pose index
IplImage* GetPatch(int index);
// GetPose: returns a pose corresponding to specified pose index
// - index: pose index
// - return value: the pose corresponding to specified pose index
CvAffinePose GetPose(int index) const;
// Save: saves all patches with different poses to a specified path
void Save(const char* path);
// ReadByName: reads a descriptor from a file storage
// - fs: file storage
// - parent: parent node
// - name: node name
// - return value: 1 if succeeded, 0 otherwise
int ReadByName(CvFileStorage* fs, CvFileNode* parent, const char* name);
// Write: writes a descriptor into a file storage
// - fs: file storage
// - name: node name
void Write(CvFileStorage* fs, const char* name);
// GetFeatureName: returns a name corresponding to a feature
const char* GetFeatureName() const;
// GetCenter: returns the center of the feature
CvPoint GetCenter() const;
void SetPCADimHigh(int pca_dim_high) {m_pca_dim_high = pca_dim_high;};
void SetPCADimLow(int pca_dim_low) {m_pca_dim_low = pca_dim_low;};
int GetPCADimLow() const;
int GetPCADimHigh() const;
CvMat** GetPCACoeffs() const {return m_pca_coeffs;}
protected:
int m_pose_count; // the number of poses
CvSize m_patch_size; // size of each image
IplImage** m_samples; // an array of length m_pose_count containing the patch in different poses
IplImage* m_input_patch;
IplImage* m_train_patch;
CvMat** m_pca_coeffs; // an array of length m_pose_count containing pca decomposition of the patch in different poses
CvAffinePose* m_affine_poses; // an array of poses
CvMat** m_transforms; // an array of affine transforms corresponding to poses
std::string m_feature_name; // the name of the feature associated with the descriptor
CvPoint m_center; // the coordinates of the feature (the center of the input image ROI)
int m_pca_dim_high; // the number of descriptor pca components to use for generating affine poses
int m_pca_dim_low; // the number of pca components to use for comparison
};
// OneWayDescriptorBase: encapsulates functionality for training/loading a set of one way descriptors
// and finding the nearest closest descriptor to an input feature
class CV_EXPORTS OneWayDescriptorBase
{
public:
// creates an instance of OneWayDescriptor from a set of training files
// - patch_size: size of the input (large) patch
// - pose_count: the number of poses to generate for each descriptor
// - train_path: path to training files
// - pca_config: the name of the file that contains PCA for small patches (2 times smaller
// than patch_size each dimension
// - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
// - pca_desc_config: the name of the file that contains descriptors of PCA components
OneWayDescriptorBase(CvSize patch_size, int pose_count, const char* train_path = 0, const char* pca_config = 0,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1,
int pca_dim_high = 100, int pca_dim_low = 100);
~OneWayDescriptorBase();
// Allocate: allocates memory for a given number of descriptors
void Allocate(int train_feature_count);
// AllocatePCADescriptors: allocates memory for pca descriptors
void AllocatePCADescriptors();
// returns patch size
CvSize GetPatchSize() const {return m_patch_size;};
// returns the number of poses for each descriptor
int GetPoseCount() const {return m_pose_count;};
// returns the number of pyramid levels
int GetPyrLevels() const {return m_pyr_levels;};
// returns the number of descriptors
int GetDescriptorCount() const {return m_train_feature_count;};
// CreateDescriptorsFromImage: creates descriptors for each of the input features
// - src: input image
// - features: input features
// - pyr_levels: the number of pyramid levels
void CreateDescriptorsFromImage(IplImage* src, const std::vector<cv::KeyPoint>& features);
// CreatePCADescriptors: generates descriptors for PCA components, needed for fast generation of feature descriptors
void CreatePCADescriptors();
// returns a feature descriptor by feature index
const OneWayDescriptor* GetDescriptor(int desc_idx) const {return &m_descriptors[desc_idx];};
// FindDescriptor: finds the closest descriptor
// - patch: input image patch
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
// - _scales: scales of the input patch for each descriptor
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int& desc_idx, int& pose_idx, float& distance, float* _scale = 0, float* scale_ranges = 0) const;
// - patch: input image patch
// - n: number of the closest indexes
// - desc_idxs: output indexes of the closest descriptor to the input patch (n)
// - pose_idx: output indexes of the closest pose of the closest descriptor to the input patch (n)
// - distances: distance from the input patch to the closest feature pose (n)
// - _scales: scales of the input patch
// - scale_ranges: input scales variation (float[2])
void FindDescriptor(IplImage* patch, int n, std::vector<int>& desc_idxs, std::vector<int>& pose_idxs,
std::vector<float>& distances, std::vector<float>& _scales, float* scale_ranges = 0) const;
// FindDescriptor: finds the closest descriptor
// - src: input image
// - pt: center of the feature
// - desc_idx: output index of the closest descriptor to the input patch
// - pose_idx: output index of the closest pose of the closest descriptor to the input patch
// - distance: distance from the input patch to the closest feature pose
void FindDescriptor(IplImage* src, cv::Point2f pt, int& desc_idx, int& pose_idx, float& distance) const;
// InitializePoses: generates random poses
void InitializePoses();
// InitializeTransformsFromPoses: generates 2x3 affine matrices from poses (initializes m_transforms)
void InitializeTransformsFromPoses();
// InitializePoseTransforms: subsequently calls InitializePoses and InitializeTransformsFromPoses
void InitializePoseTransforms();
// InitializeDescriptor: initializes a descriptor
// - desc_idx: descriptor index
// - train_image: image patch (ROI is supported)
// - feature_label: feature textual label
void InitializeDescriptor(int desc_idx, IplImage* train_image, const char* feature_label);
void InitializeDescriptor(int desc_idx, IplImage* train_image, const cv::KeyPoint& keypoint, const char* feature_label);
// InitializeDescriptors: load features from an image and create descriptors for each of them
void InitializeDescriptors(IplImage* train_image, const vector<cv::KeyPoint>& features,
const char* feature_label = "", int desc_start_idx = 0);
// LoadPCADescriptors: loads PCA descriptors from a file
// - filename: input filename
int LoadPCADescriptors(const char* filename);
// SavePCADescriptors: saves PCA descriptors to a file
// - filename: output filename
void SavePCADescriptors(const char* filename);
// SetPCAHigh: sets the high resolution pca matrices (copied to internal structures)
void SetPCAHigh(CvMat* avg, CvMat* eigenvectors);
// SetPCALow: sets the low resolution pca matrices (copied to internal structures)
void SetPCALow(CvMat* avg, CvMat* eigenvectors);
int GetLowPCA(CvMat** avg, CvMat** eigenvectors)
{
*avg = m_pca_avg;
*eigenvectors = m_pca_eigenvectors;
return m_pca_dim_low;
};
int GetPCADimLow() const {return m_pca_dim_low;};
int GetPCADimHigh() const {return m_pca_dim_high;};
void ConvertDescriptorsArrayToTree(); // Converting pca_descriptors array to KD tree
protected:
CvSize m_patch_size; // patch size
int m_pose_count; // the number of poses for each descriptor
int m_train_feature_count; // the number of the training features
OneWayDescriptor* m_descriptors; // array of train feature descriptors
CvMat* m_pca_avg; // PCA average Vector for small patches
CvMat* m_pca_eigenvectors; // PCA eigenvectors for small patches
CvMat* m_pca_hr_avg; // PCA average Vector for large patches
CvMat* m_pca_hr_eigenvectors; // PCA eigenvectors for large patches
OneWayDescriptor* m_pca_descriptors; // an array of PCA descriptors
cv::cvflann::Index* m_pca_descriptors_tree;
CvMat* m_pca_descriptors_matrix;
CvAffinePose* m_poses; // array of poses
CvMat** m_transforms; // array of affine transformations corresponding to poses
int m_pca_dim_high;
int m_pca_dim_low;
int m_pyr_levels;
};
class CV_EXPORTS OneWayDescriptorObject : public OneWayDescriptorBase
{
public:
// creates an instance of OneWayDescriptorObject from a set of training files
// - patch_size: size of the input (large) patch
// - pose_count: the number of poses to generate for each descriptor
// - train_path: path to training files
// - pca_config: the name of the file that contains PCA for small patches (2 times smaller
// than patch_size each dimension
// - pca_hr_config: the name of the file that contains PCA for large patches (of patch_size size)
// - pca_desc_config: the name of the file that contains descriptors of PCA components
OneWayDescriptorObject(CvSize patch_size, int pose_count, const char* train_path, const char* pca_config,
const char* pca_hr_config = 0, const char* pca_desc_config = 0, int pyr_levels = 1);
~OneWayDescriptorObject();
// Allocate: allocates memory for a given number of features
// - train_feature_count: the total number of features
// - object_feature_count: the number of features extracted from the object
void Allocate(int train_feature_count, int object_feature_count);
void SetLabeledFeatures(const vector<cv::KeyPoint>& features) {m_train_features = features;};
vector<cv::KeyPoint>& GetLabeledFeatures() {return m_train_features;};
const vector<cv::KeyPoint>& GetLabeledFeatures() const {return m_train_features;};
vector<cv::KeyPoint> _GetLabeledFeatures() const;
// IsDescriptorObject: returns 1 if descriptor with specified index is positive, otherwise 0
int IsDescriptorObject(int desc_idx) const;
// MatchPointToPart: returns the part number of a feature if it matches one of the object parts, otherwise -1
int MatchPointToPart(CvPoint pt) const;
// GetDescriptorPart: returns the part number of the feature corresponding to a specified descriptor
// - desc_idx: descriptor index
int GetDescriptorPart(int desc_idx) const;
void InitializeObjectDescriptors(IplImage* train_image, const vector<cv::KeyPoint>& features,
const char* feature_label, int desc_start_idx = 0, float scale = 1.0f,
int is_background = 0);
// GetObjectFeatureCount: returns the number of object features
int GetObjectFeatureCount() const {return m_object_feature_count;};
protected:
int* m_part_id; // contains part id for each of object descriptors
vector<cv::KeyPoint> m_train_features; // train features
int m_object_feature_count; // the number of the positive features
};
/****************************************************************************************\
* FeatureDetector *
\****************************************************************************************/
/*
* Abstract base class for 2D image feature detectors.
*/
class CV_EXPORTS FeatureDetector
{
public:
/*
* Detect keypoints in an image.
*
* image The image.
* keypoints The detected keypoints.
* mask Mask specifying where to look for keypoints (optional). Must be a char matrix with non-zero values in the region of interest.
*/
void detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const
{
detectImpl( image, mask, keypoints );
}
protected:
/*
* Detect keypoints; detect() calls this. Must be implemented by the subclass.
*/
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const = 0;
/*
* Remove keypoints that are not in the mask.
*
* Helper function, useful when wrapping a library call for keypoint detection that
* does not support a mask argument.
*/
static void removeInvalidPoints( const Mat& mask, vector<KeyPoint>& keypoints );
};
class CV_EXPORTS FastFeatureDetector : public FeatureDetector
{
public:
FastFeatureDetector( int _threshold, bool _nonmaxSuppression = true );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
int threshold;
bool nonmaxSuppression;
};
class CV_EXPORTS GoodFeaturesToTrackDetector : public FeatureDetector
{
public:
GoodFeaturesToTrackDetector( int _maxCorners, double _qualityLevel, double _minDistance,
int _blockSize=3, bool _useHarrisDetector=false, double _k=0.04 );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double k;
};
class CV_EXPORTS MserFeatureDetector : public FeatureDetector
{
public:
MserFeatureDetector( int delta, int minArea, int maxArea, float maxVariation, float minDiversity,
int maxEvolution, double areaThreshold, double minMargin, int edgeBlurSize );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
MSER mser;
};
class CV_EXPORTS StarFeatureDetector : public FeatureDetector
{
public:
StarFeatureDetector( int maxSize=16, int responseThreshold=30, int lineThresholdProjected = 10,
int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
StarDetector star;
};
class CV_EXPORTS SiftFeatureDetector : public FeatureDetector
{
public:
SiftFeatureDetector( double threshold=SIFT::DetectorParams::GET_DEFAULT_THRESHOLD(),
double edgeThreshold=SIFT::DetectorParams::GET_DEFAULT_EDGE_THRESHOLD(),
int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
SIFT sift;
};
class CV_EXPORTS SurfFeatureDetector : public FeatureDetector
{
public:
SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3, int octaveLayers = 4 );
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
SURF surf;
};
/****************************************************************************************\
* DescriptorExtractor *
\****************************************************************************************/
/*
* Abstract base class for computing descriptors for image keypoints.
*
* In this interface we assume a keypoint descriptor can be represented as a
* dense, fixed-dimensional vector of some basic type. Most descriptors used
* in practice follow this pattern, as it makes it very easy to compute
* distances between descriptors. Therefore we represent a collection of
* descriptors as a cv::Mat, where each row is one keypoint descriptor.
*/
class CV_EXPORTS DescriptorExtractor
{
public:
/*
* Compute the descriptors for a set of keypoints in an image.
*
* Must be implemented by the subclass.
*
* image The image.
* keypoints The keypoints. Keypoints for which a descriptor cannot be computed are removed.
* descriptors The descriptors. Row i is the descriptor for keypoint i.
*/
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
protected:
/*
* Remove keypoints within border_pixels of an image edge.
*/
static void removeBorderKeypoints( vector<KeyPoint>& keypoints,
Size imageSize, int borderPixels );
};
class CV_EXPORTS SiftDescriptorExtractor : public DescriptorExtractor
{
public:
SiftDescriptorExtractor( double magnification=SIFT::DescriptorParams::GET_DEFAULT_MAGNIFICATION(),
bool isNormalize=true, bool recalculateAngles=true,
int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
int angleMode=SIFT::CommonParams::FIRST_ANGLE );
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const;
protected:
SIFT sift;
};
class CV_EXPORTS SurfDescriptorExtractor : public DescriptorExtractor
{
public:
SurfDescriptorExtractor( int nOctaves=4,
int nOctaveLayers=2, bool extended=false );
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const;
protected:
SURF surf;
};
/****************************************************************************************\
* Distance *
\****************************************************************************************/
template<typename T>
struct CV_EXPORTS Accumulator
{
typedef T Type;
};
template<> struct Accumulator<unsigned char> { typedef unsigned int Type; };
template<> struct Accumulator<unsigned short> { typedef unsigned int Type; };
template<> struct Accumulator<char> { typedef int Type; };
template<> struct Accumulator<short> { typedef int Type; };
/*
* Squared Euclidean distance functor
*/
template<class T>
struct CV_EXPORTS L2
{
typedef T ValueType;
typedef typename Accumulator<T>::Type ResultType;
ResultType operator()( const T* a, const T* b, int size ) const
{
ResultType result = ResultType();
for( int i = 0; i < size; i++ )
{
ResultType diff = a[i] - b[i];
result += diff*diff;
}
return result;
}
};
/****************************************************************************************\
* DescriptorMatcher *
\****************************************************************************************/
/*
* Abstract base class for matching two sets of descriptors.
*/
class CV_EXPORTS DescriptorMatcher
{
public:
/*
* Add descriptors to the training set
* descriptors Descriptors to add to the training set
*/
void add( const Mat& descriptors );
/*
* Index the descriptors training set
*/
void index();
/*
* Find the best match for each descriptor from a query set
*
* query The query set of descriptors
* matches Indices of the closest matches from the training set
*/
void match( const Mat& query, vector<int>& matches ) const;
/*
* Find the best matches between two descriptor sets, with constraints
* on which pairs of descriptors can be matched.
*
* The mask describes which descriptors can be matched. descriptors_1[i]
* can be matched with descriptors_2[j] only if mask.at<char>(i,j) is non-zero.
*
* query The query set of descriptors
* mask Mask specifying permissible matches.
* matches Indices of the closest matches from the training set
*/
void match( const Mat& query, const Mat& mask,
vector<int>& matches, vector<double>* distances = 0 ) const;
/*
* Find the best keypoint matches for small view changes.
*
* This function will only match descriptors whose keypoints have close enough
* image coordinates.
*
* keypoints_1 The first set of keypoints.
* descriptors_1 The first set of descriptors.
* keypoints_2 The second set of keypoints.
* descriptors_2 The second set of descriptors.
* maxDeltaX The maximum horizontal displacement.
* maxDeltaY The maximum vertical displacement.
* matches The matches between both sets.
*/
/*void matchWindowed( const vector<KeyPoint>& keypoints_1, const Mat& descriptors_1,
const vector<KeyPoint>& keypoints_2, const Mat& descriptors_2,
float maxDeltaX, float maxDeltaY, vector<Match>& matches) const;*/
protected:
Mat train;
/*
* Find matches; match() calls this. Must be implemented by the subclass.
* The mask may be empty.
*/
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches, vector<double>& distances ) const = 0;
static bool possibleMatch( const Mat& mask, int index_1, int index_2 )
{
return mask.empty() || mask.at<char>(index_1, index_2);
}
};
inline void DescriptorMatcher::add( const Mat& descriptors )
{
if( train.empty() )
{
train = descriptors;
}
else
{
// merge train and descriptors
Mat m( train.rows + descriptors.rows, train.cols, CV_32F );
Mat m1 = m.rowRange( 0, train.rows );
train.copyTo( m1 );
Mat m2 = m.rowRange( train.rows + 1, m.rows );
descriptors.copyTo( m2 );
train = m;
}
}
inline void DescriptorMatcher::match( const Mat& query, vector<int>& matches ) const
{
vector<double> innDistances;
matchImpl( query, train, Mat(), matches, innDistances );
}
inline void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<int>& matches, vector<double>* distances ) const
{
if( distances )
matchImpl( query, train, mask, matches, *distances );
else
{
vector<double> innDistances;
matchImpl( query, train, mask, matches, innDistances );
}
}
/*
* Brute-force descriptor matcher.
*
* For each descriptor in the first set, this matcher finds the closest
* descriptor in the second set by trying each one.
*
* For efficiency, BruteForceMatcher is templated on the distance metric.
* For float descriptors, a common choice would be features_2d::L2<float>.
*/
template<class Distance>
class CV_EXPORTS BruteForceMatcher : public DescriptorMatcher
{
public:
BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
protected:
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches, vector<double>& distances) const;
Distance distance;
};
template<class Distance>
void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches,
vector<double>& distances) const
{
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
assert( mask.empty() || (mask.rows == descriptors_1.rows && mask.cols == descriptors_2.rows) );
assert( descriptors_1.cols == descriptors_2.cols );
assert( DataType<ValueType>::type == descriptors_1.type() || descriptors_1.empty() );
assert( DataType<ValueType>::type == descriptors_2.type() || descriptors_2.empty() );
int dimension = descriptors_1.cols;
matches.clear();
matches.reserve(descriptors_1.rows);
for( int i = 0; i < descriptors_1.rows; i++ )
{
const ValueType* d1 = descriptors_1.ptr<ValueType>(i);
int matchIndex = -1;
DistanceType matchDistance = std::numeric_limits<DistanceType>::max();
for( int j = 0; j < descriptors_2.rows; j++ )
{
if( possibleMatch(mask, i, j) )
{
const ValueType* d2 = descriptors_2.ptr<ValueType>(j);
DistanceType curDistance = distance(d1, d2, dimension);
if( curDistance < matchDistance )
{
matchDistance = curDistance;
matchIndex = j;
}
}
}
if( matchIndex != -1 )
{
matches.push_back( matchIndex );
distances.push_back( matchDistance );
}
}
}
/****************************************************************************************\
* GenericDescriptorMatch *
\****************************************************************************************/
/*
* A storage for sets of keypoints together with corresponding images and class IDs
*/
class CV_EXPORTS KeyPointCollection
{
public:
// Adds keypoints from a single image to the storage
// image Source image
// points A vector of keypoints
void add( const Mat& _image, const vector<KeyPoint>& _points );
// Returns the total number of keypoints in the collection
size_t calcKeypointCount() const;
// Returns the keypoint by its global index
KeyPoint getKeyPoint( int index ) const;
// Clears images, keypoints and startIndices
void clear();
vector<Mat> images;
vector<vector<KeyPoint> > points;
// global indices of the first points in each image,
// startIndices.size() = points.size()
vector<int> startIndices;
};
/*
* Abstract interface for a keypoint descriptor
*/
class CV_EXPORTS GenericDescriptorMatch
{
public:
enum IndexType
{
NoIndex,
KDTreeIndex
};
GenericDescriptorMatch() {}
virtual ~GenericDescriptorMatch() {}
// Adds keypoints to the training set (descriptors are supposed to be calculated here)
virtual void add( KeyPointCollection& keypoints );
// Adds keypoints from a single image to the training set (descriptors are supposed to be calculated here)
virtual void add( const Mat& image, vector<KeyPoint>& points ) = 0;
// Classifies test keypoints
// image The source image
// points Test keypoints from the source image
virtual void classify( const Mat& image, vector<KeyPoint>& points );
// Matches test keypoints to the training set
// image The source image
// points Test keypoints from the source image
// class_ids A vector to be filled with keypoint class_ids
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<int>& indices ) = 0;
// Clears keypoints storing in collection
virtual void clear();
protected:
KeyPointCollection collection;
};
/*
* OneWayDescriptorMatch
*/
class CV_EXPORTS OneWayDescriptorMatch : public GenericDescriptorMatch
{
public:
class Params
{
public:
static const int POSE_COUNT = 500;
static const int PATCH_WIDTH = 24;
static const int PATCH_HEIGHT = 24;
static float GET_MIN_SCALE() { return 1.f; }
static float GET_MAX_SCALE() { return 3.f; }
static float GET_STEP_SCALE() { return 1.15f; }
Params( int _poseCount = POSE_COUNT,
Size _patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
string _trainPath = string(),
string _pcaConfig = string(), string _pcaHrConfig = string(),
string _pcaDescConfig = string(),
float _minScale = GET_MIN_SCALE(), float _maxScale = GET_MAX_SCALE(),
float _stepScale = GET_STEP_SCALE() ) :
poseCount(_poseCount), patchSize(_patchSize), trainPath(_trainPath),
pcaConfig(_pcaConfig), pcaHrConfig(_pcaHrConfig), pcaDescConfig(_pcaDescConfig),
minScale(_minScale), maxScale(_maxScale), stepScale(_stepScale) {}
int poseCount;
Size patchSize;
string trainPath;
string pcaConfig, pcaHrConfig, pcaDescConfig;
float minScale, maxScale, stepScale;
};
OneWayDescriptorMatch();
// Equivalent to calling PointMatchOneWay() followed by Initialize(_params)
OneWayDescriptorMatch( const Params& _params );
virtual ~OneWayDescriptorMatch();
// Sets one way descriptor parameters
void initialize( const Params& _params );
// Calculates one way descriptors for a set of keypoints
virtual void add( const Mat& image, vector<KeyPoint>& keypoints );
// Calculates one way descriptors for a set of keypoints
virtual void add( KeyPointCollection& keypoints );
// Matches a set of keypoints from a single image of the training set. A rectangle with a center in a keypoint
// and size (patch_width/2*scale, patch_height/2*scale) is cropped from the source image for each
// keypoint. scale is iterated from DescriptorOneWayParams::min_scale to DescriptorOneWayParams::max_scale.
// The minimum distance to each training patch with all its affine poses is found over all scales.
// The class ID of a match is returned for each keypoint. The distance is calculated over PCA components
// loaded with DescriptorOneWay::Initialize, kd tree is used for finding minimum distances.
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<int>& indices );
// Classify a set of keypoints. The same as match, but returns point classes rather than indices
virtual void classify( const Mat& image, vector<KeyPoint>& points );
protected:
Ptr<OneWayDescriptorBase> base;
Params params;
};
/*
* CalonderDescriptorMatch
*/
class CV_EXPORTS CalonderDescriptorMatch : public GenericDescriptorMatch
{
public:
class Params
{
public:
static const int DEFAULT_NUM_TREES = 80;
static const int DEFAULT_DEPTH = 9;
static const int DEFAULT_VIEWS = 5000;
static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
static const int DEFAULT_PATCH_SIZE = PATCH_SIZE;
Params( const RNG& _rng = RNG(), const PatchGenerator& _patchGen = PatchGenerator(),
int _numTrees=DEFAULT_NUM_TREES,
int _depth=DEFAULT_DEPTH,
int _views=DEFAULT_VIEWS,
size_t _reducedNumDim=DEFAULT_REDUCED_NUM_DIM,
int _numQuantBits=DEFAULT_NUM_QUANT_BITS,
bool _printStatus=true,
int _patchSize=DEFAULT_PATCH_SIZE );
Params( const string& _filename );
RNG rng;
PatchGenerator patchGen;
int numTrees;
int depth;
int views;
int patchSize;
size_t reducedNumDim;
int numQuantBits;
bool printStatus;
string filename;
};
CalonderDescriptorMatch();
CalonderDescriptorMatch( const Params& _params );
virtual ~CalonderDescriptorMatch();
void initialize( const Params& _params );
virtual void add( const Mat& image, vector<KeyPoint>& keypoints );
virtual void match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices );
virtual void classify( const Mat& image, vector<KeyPoint>& keypoints );
protected:
void trainRTreeClassifier();
Mat extractPatch( const Mat& image, const Point& pt, int patchSize ) const;
void calcBestProbAndMatchIdx( const Mat& image, const Point& pt,
float& bestProb, int& bestMatchIdx, float* signature );
Ptr<RTreeClassifier> classifier;
Params params;
};
/*
* FernDescriptorMatch
*/
class CV_EXPORTS FernDescriptorMatch : public GenericDescriptorMatch
{
public:
class Params
{
public:
Params( int _nclasses=0,
int _patchSize=FernClassifier::PATCH_SIZE,
int _signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
int _nstructs=FernClassifier::DEFAULT_STRUCTS,
int _structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int _nviews=FernClassifier::DEFAULT_VIEWS,
int _compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const string& _filename );
int nclasses;
int patchSize;
int signatureSize;
int nstructs;
int structSize;
int nviews;
int compressionMethod;
PatchGenerator patchGenerator;
string filename;
};
FernDescriptorMatch();
FernDescriptorMatch( const Params& _params );
virtual ~FernDescriptorMatch();
void initialize( const Params& _params );
virtual void add( const Mat& image, vector<KeyPoint>& keypoints );
virtual void match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices );
virtual void classify( const Mat& image, vector<KeyPoint>& keypoints );
protected:
void trainFernClassifier();
void calcBestProbAndMatchIdx( const Mat& image, const Point2f& pt,
float& bestProb, int& bestMatchIdx, vector<float>& signature );
Ptr<FernClassifier> classifier;
Params params;
};
/****************************************************************************************\
* VectorDescriptorMatch *
\****************************************************************************************/
/*
* A class used for matching descriptors that can be described as vectors in a finite-dimensional space
*/
template<class Extractor, class Matcher>
class CV_EXPORTS VectorDescriptorMatch : public GenericDescriptorMatch
{
public:
using GenericDescriptorMatch::add;
VectorDescriptorMatch( const Extractor& _extractor = Extractor(), const Matcher& _matcher = Matcher() ) :
extractor(_extractor), matcher(_matcher) {}
~VectorDescriptorMatch() {}
// Builds flann index
void index();
// Calculates descriptors for a set of keypoints from a single image
virtual void add( const Mat& image, vector<KeyPoint>& keypoints )
{
Mat descriptors;
extractor.compute( image, keypoints, descriptors );
matcher.add( descriptors );
collection.add( Mat(), keypoints );
};
// Matches a set of keypoints with the training set
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<int>& keypointIndices )
{
Mat descriptors;
extractor.compute( image, points, descriptors );
matcher.match( descriptors, keypointIndices );
};
protected:
Extractor extractor;
Matcher matcher;
vector<int> classIds;
};
}
#endif /* __cplusplus */
#endif
/* End of file. */
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