mirror of
https://github.com/opencv/opencv.git
synced 2026-07-30 15:53:03 +04:00
modified FernClassifier::train(); remove old RTreeClassifier and added new implementation CalonderClassifier; removed old find_obj_calonder and added new one
This commit is contained in:
Maria Dimashova
@@ -179,6 +179,17 @@ CVAPI(CvSeq*) cvGetStarKeypoints( const CvArr* img, CvMemStorage* storage,
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namespace cv
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{
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struct CV_EXPORTS DefaultRngAuto
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{
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const static uint64 def_state = (uint64)-1;
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const uint64 old_state;
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DefaultRngAuto() : old_state(theRNG().state) { theRNG().state = def_state; }
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~DefaultRngAuto() { theRNG().state = old_state; }
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DefaultRngAuto& operator=(const DefaultRngAuto&);
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};
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// CvAffinePose: defines a parameterized affine transformation of an image patch.
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// An image patch is rotated on angle phi (in degrees), then scaled lambda1 times
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@@ -395,10 +406,7 @@ public:
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CV_EXPORTS void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true );
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/*!
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The Patch Generator class
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The Patch Generator class
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*/
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class CV_EXPORTS PatchGenerator
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{
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@@ -459,9 +467,9 @@ class CV_EXPORTS FernClassifier
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public:
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FernClassifier();
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FernClassifier(const FileNode& node);
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FernClassifier(const vector<Point2f>& points,
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const vector<Ptr<Mat> >& refimgs,
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const vector<int>& labels=vector<int>(),
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FernClassifier(const vector<vector<Point2f> >& points,
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const vector<Mat>& refimgs,
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const vector<vector<int> >& labels=vector<vector<int> >(),
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int _nclasses=0, int _patchSize=PATCH_SIZE,
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int _signatureSize=DEFAULT_SIGNATURE_SIZE,
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int _nstructs=DEFAULT_STRUCTS,
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@@ -481,9 +489,9 @@ public:
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int _nviews=DEFAULT_VIEWS,
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int _compressionMethod=COMPRESSION_NONE,
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const PatchGenerator& patchGenerator=PatchGenerator());
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virtual void train(const vector<Point2f>& points,
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const vector<Ptr<Mat> >& refimgs,
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const vector<int>& labels=vector<int>(),
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virtual void train(const vector<vector<Point2f> >& points,
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const vector<Mat>& refimgs,
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const vector<vector<int> >& labels=vector<vector<int> >(),
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int _nclasses=0, int _patchSize=PATCH_SIZE,
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int _signatureSize=DEFAULT_SIGNATURE_SIZE,
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int _nstructs=DEFAULT_STRUCTS,
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@@ -594,269 +602,126 @@ protected:
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FernClassifier fernClassifier;
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};
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/****************************************************************************************\
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* Calonder Descriptor *
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* Calonder Classifier *
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\****************************************************************************************/
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struct CV_EXPORTS DefaultRngAuto
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{
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const static uint64 def_state = (uint64)-1;
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const uint64 old_state;
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DefaultRngAuto() : old_state(theRNG().state) { theRNG().state = def_state; }
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~DefaultRngAuto() { theRNG().state = old_state; }
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DefaultRngAuto& operator=(const DefaultRngAuto&);
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};
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/*
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A pseudo-random number generator usable with std::random_shuffle.
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*/
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typedef cv::RNG CalonderRng;
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typedef unsigned int int_type;
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//----------------------------
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//randomized_tree.h
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//class RTTester;
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//namespace features {
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static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
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static const float LOWER_QUANT_PERC = .03f;
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static const float UPPER_QUANT_PERC = .92f;
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static const int PATCH_SIZE = 32;
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static const int DEFAULT_DEPTH = 9;
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static const int DEFAULT_VIEWS = 5000;
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struct RTreeNode;
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struct BaseKeypoint
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{
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int x;
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int y;
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IplImage* image;
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BaseKeypoint()
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: x(0), y(0), image(NULL)
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{}
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BaseKeypoint(int x, int y, IplImage* image)
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: x(x), y(y), image(image)
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{}
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};
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class CSMatrixGenerator {
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public:
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typedef enum { PDT_GAUSS=1, PDT_BERNOULLI, PDT_DBFRIENDLY } PHI_DISTR_TYPE;
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~CSMatrixGenerator();
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static float* getCSMatrix(int m, int n, PHI_DISTR_TYPE dt); // do NOT free returned pointer
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private:
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static float *cs_phi_; // matrix for compressive sensing
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static int cs_phi_m_, cs_phi_n_;
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};
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template< typename T >
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struct AlignedMemBlock
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{
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AlignedMemBlock() : raw(NULL), data(NULL) { };
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// Alloc's an `a` bytes-aligned block good to hold `sz` elements of class T
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AlignedMemBlock(const int n, const int a)
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{
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alloc(n, a);
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}
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~AlignedMemBlock()
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{
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free(raw);
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}
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void alloc(const int n, const int a)
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{
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uchar* raw = (uchar*)malloc(n*sizeof(T) + a);
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int delta = (a - uint64(raw)%a)%a; // # bytes required for padding s.t. we get `a`-aligned
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data = reinterpret_cast<T*>(raw + delta);
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}
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// Methods to access the aligned data. NEVER EVER FREE A RETURNED POINTER!
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inline T* p() { return data; }
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inline T* operator()() { return data; }
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private:
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T *raw; // raw block, probably not aligned
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T *data; // exposed data, aligned, DO NOT FREE
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};
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typedef AlignedMemBlock<float> FloatSignature;
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typedef AlignedMemBlock<uchar> Signature;
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class CV_EXPORTS RandomizedTree
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class CV_EXPORTS CalonderClassifier
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{
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public:
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friend class RTreeClassifier;
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//friend class ::RTTester;
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CalonderClassifier();
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CalonderClassifier( const vector<vector<Point2f> >& points, const vector<Mat>& refimgs,
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const vector<vector<int> >& labels=vector<vector<int> >(), int _numClasses=0,
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int _pathSize=DEFAULT_PATCH_SIZE,
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int _numTrees=DEFAULT_NUM_TREES,
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int _treeDepth=DEFAULT_TREE_DEPTH,
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int _numViews=DEFAULT_NUM_VIEWS,
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int _compressedDim=DEFAULT_COMPRESSED_DIM,
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int _compressType=DEFAULT_COMPRESS_TYPE,
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int _numQuantBits=DEFAULT_NUM_QUANT_BITS,
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const PatchGenerator& patchGenerator=PatchGenerator() );
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RandomizedTree();
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~RandomizedTree();
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virtual ~CalonderClassifier();
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virtual void clear();
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void train(std::vector<BaseKeypoint> const& base_set, cv::RNG &rng,
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int depth, int views, size_t reduced_num_dim, int num_quant_bits);
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void train( const vector<vector<Point2f> >& points, const vector<Mat>& refimgs,
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const vector<vector<int> >& labels=vector<vector<int> >(), int _nclasses=0,
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int _pathSize=DEFAULT_PATCH_SIZE,
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int _numTrees=DEFAULT_NUM_TREES,
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int _treeDepth=DEFAULT_TREE_DEPTH,
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int _numViews=DEFAULT_NUM_VIEWS,
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int _compressedDim=DEFAULT_COMPRESSED_DIM,
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int _compressType=DEFAULT_COMPRESS_TYPE,
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int _numQuantBits=DEFAULT_NUM_QUANT_BITS,
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const PatchGenerator& patchGenerator=PatchGenerator() );
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void train(std::vector<BaseKeypoint> const& base_set, cv::RNG &rng,
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PatchGenerator &make_patch, int depth, int views, size_t reduced_num_dim,
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int num_quant_bits);
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virtual void operator()(const Mat& img, Point2f pt, vector<float>& signature, float thresh=0.f) const;
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virtual void operator()(const Mat& patch, vector<float>& signature, float thresh=-1.f) const;
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#define QUANTIZATION_AVAILABLE 1
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#if QUANTIZATION_AVAILABLE
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void quantizePosteriors( int _numQuantBits, bool isClearFloatPosteriors=false );
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void clearFloatPosteriors();
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virtual void operator()(const Mat& img, Point2f pt, vector<uchar>& signature, uchar thresh=-1.f) const;
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virtual void operator()(const Mat& patch, vector<uchar>& signature, uchar thresh=-1.f) const;
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#endif
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// following two funcs are EXPERIMENTAL (do not use unless you know exactly what you do)
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static void quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode=0);
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static void quantizeVector(float *src, int dim, int N, float bnds[2], uchar *dst);
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void read( const FileNode& fn );
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void write( FileStorage& fs ) const;
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// patch_data must be a 32x32 array (no row padding)
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float* getPosterior(uchar* patch_data);
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const float* getPosterior(uchar* patch_data) const;
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uchar* getPosterior2(uchar* patch_data);
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bool empty() const;
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void read(const char* file_name, int num_quant_bits);
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void read(std::istream &is, int num_quant_bits);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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void setVerbose( bool _verbose );
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inline int classes() { return classes_; }
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inline int depth() { return depth_; }
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int getPatchSize() const;
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int getNumTrees() const;
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int getTreeDepth() const;
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int getNumViews() const;
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int getSignatureSize() const;
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int getCompressType() const;
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int getNumQuantBits() const;
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int getOrigNumClasses() const;
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inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); }
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// debug
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void savePosteriors(std::string url, bool append=false);
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void savePosteriors2(std::string url, bool append=false);
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private:
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int classes_;
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int depth_;
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int num_leaves_;
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std::vector<RTreeNode> nodes_;
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//float **posteriors_; // 16-bytes aligned posteriors
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//uchar **posteriors2_; // 16-bytes aligned posteriors
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FloatSignature *posteriors_;
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Signature *posteriors2_;
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std::vector<int> leaf_counts_;
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void createNodes(int num_nodes, cv::RNG &rng);
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void allocPosteriorsAligned(int num_leaves, int num_classes);
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void freePosteriors(int which); // which: 1=posteriors_, 2=posteriors2_, 3=both
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void init(int classes, int depth, cv::RNG &rng);
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void addExample(int class_id, uchar* patch_data);
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void finalize(size_t reduced_num_dim, int num_quant_bits);
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int getIndex(uchar* patch_data) const;
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inline float* getPosteriorByIndex(int index);
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inline uchar* getPosteriorByIndex2(int index);
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inline const float* getPosteriorByIndex(int index) const;
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//void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
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void convertPosteriorsToChar();
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void makePosteriors2(int num_quant_bits);
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void compressLeaves(size_t reduced_num_dim);
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void estimateQuantPercForPosteriors(float perc[2]);
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};
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struct RTreeNode
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{
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short offset1, offset2;
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RTreeNode() {}
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RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
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: offset1(y1*PATCH_SIZE + x1),
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offset2(y2*PATCH_SIZE + x2)
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{}
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//! Left child on 0, right child on 1
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inline bool operator() (uchar* patch_data) const
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enum
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{
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return patch_data[offset1] > patch_data[offset2];
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}
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};
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COMPRESS_NONE = -1,
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COMPRESS_DISTR_GAUSS = 0,
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COMPRESS_DISTR_BERNOULLI = 1,
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COMPRESS_DISTR_DBFRIENDLY = 2,
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};
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static float GET_LOWER_QUANT_PERC() { return .03f; }
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static float GET_UPPER_QUANT_PERC() { return .92f; }
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//} // namespace features
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//----------------------------
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//rtree_classifier.h
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//class RTTester;
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//namespace features {
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class CV_EXPORTS RTreeClassifier
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{
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public:
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//friend class ::RTTester;
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static const int DEFAULT_TREES = 80;
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static const size_t DEFAULT_NUM_QUANT_BITS = 4;
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//static const int SIG_LEN = 176;
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RTreeClassifier();
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//modified
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng,
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int num_trees = RTreeClassifier::DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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void train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng,
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PatchGenerator &make_patch,
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int num_trees = DEFAULT_TREES,
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int depth = DEFAULT_DEPTH,
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int views = DEFAULT_VIEWS,
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size_t reduced_num_dim = DEFAULT_REDUCED_NUM_DIM,
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int num_quant_bits = DEFAULT_NUM_QUANT_BITS,
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bool print_status = true);
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// sig must point to a memory block of at least classes()*sizeof(float|uchar) bytes
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void getSignature(IplImage *patch, uchar *sig);
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void getSignature(IplImage *patch, float *sig);
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void getSparseSignature(IplImage *patch, float *sig, float thresh);
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// TODO: deprecated in favor of getSignature overload, remove
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void getFloatSignature(IplImage *patch, float *sig) { getSignature(patch, sig); }
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static int countNonZeroElements(float *vec, int n, double tol=1e-10);
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static inline void safeSignatureAlloc(uchar **sig, int num_sig=1, int sig_len=176);
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static inline uchar* safeSignatureAlloc(int num_sig=1, int sig_len=176);
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inline int classes() const { return classes_; }
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inline int original_num_classes() { return original_num_classes_; }
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void setQuantization(int num_quant_bits);
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void discardFloatPosteriors();
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void read(const char* file_name);
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void read(std::istream &is);
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void write(const char* file_name) const;
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void write(std::ostream &os) const;
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// experimental and debug
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void saveAllFloatPosteriors(std::string file_url);
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void saveAllBytePosteriors(std::string file_url);
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void setFloatPosteriorsFromTextfile_176(std::string url);
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float countZeroElements();
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std::vector<RandomizedTree> trees_;
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enum
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{
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MAX_NUM_QUANT_BITS = 8,
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DEFAULT_PATCH_SIZE = 32,
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DEFAULT_NUM_TREES = 48,
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DEFAULT_TREE_DEPTH = 9,
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DEFAULT_NUM_VIEWS = 500,
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DEFAULT_COMPRESSED_DIM = 176,
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DEFAULT_COMPRESS_TYPE = COMPRESS_DISTR_BERNOULLI,
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DEFAULT_NUM_QUANT_BITS = -1,
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};
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private:
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int classes_;
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int num_quant_bits_;
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uchar **posteriors_;
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ushort *ptemp_;
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int original_num_classes_;
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bool keep_floats_;
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};
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void prepare( int _patchSize, int _signatureSize, int _numTrees, int _treeDepth, int _numViews );
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int getLeafIdx( int treeIdx, const Mat& patch ) const;
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void finalize( int _compressedDim, int _compressType, int _numQuantBits,
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const vector<int>& leafSampleCounters);
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|
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void compressLeaves( int _compressedDim, int _compressType );
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bool verbose;
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int patchSize;
|
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int signatureSize;
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int numTrees;
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int treeDepth;
|
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int numViews;
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|
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int origNumClasses;
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int compressType;
|
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int numQuantBits;
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|
||||
int numLeavesPerTree;
|
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int numNodesPerTree;
|
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|
||||
struct Node
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||||
{
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uchar x1, y1, x2, y2;
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||||
Node() : x1(0), y1(0), x2(0), y2(0) {}
|
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Node( uchar _x1, uchar _y1, uchar _x2, uchar _y2 ) : x1(_x1), y1(_y1), x2(_x2), y2(_y2)
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{}
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int operator() (const Mat_<uchar>& patch) const
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{ return patch(y1,x1) > patch(y2, x2) ? 1 : 0; }
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};
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vector<Node> nodes;
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vector<float> posteriors;
|
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#if QUANTIZATION_AVAILABLE
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vector<uchar> quantizedPosteriors;
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#endif
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||||
};
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|
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/****************************************************************************************\
|
||||
* One-Way Descriptor *
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||||
@@ -1004,7 +869,7 @@ protected:
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CvAffinePose* m_affine_poses; // an array of poses
|
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CvMat** m_transforms; // an array of affine transforms corresponding to poses
|
||||
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std::string m_feature_name; // the name of the feature associated with the descriptor
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string m_feature_name; // the name of the feature associated with the descriptor
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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
|
||||
@@ -1275,7 +1140,8 @@ public:
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||||
*
|
||||
* 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.
|
||||
* 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
|
||||
{
|
||||
@@ -1430,8 +1296,8 @@ public:
|
||||
*/
|
||||
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
|
||||
|
||||
virtual void read (const FileNode&) {};
|
||||
virtual void write (FileStorage&) const {};
|
||||
virtual void read( const FileNode& ) {};
|
||||
virtual void write( FileStorage& ) const {};
|
||||
|
||||
protected:
|
||||
/*
|
||||
@@ -1451,9 +1317,9 @@ public:
|
||||
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;
|
||||
virtual void read (const FileNode &fn);
|
||||
virtual void write (FileStorage &fs) const;
|
||||
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage &fs ) const;
|
||||
|
||||
protected:
|
||||
SIFT sift;
|
||||
@@ -1465,14 +1331,56 @@ public:
|
||||
SurfDescriptorExtractor( int nOctaves=4,
|
||||
int nOctaveLayers=2, bool extended=false );
|
||||
|
||||
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const;
|
||||
virtual void read (const FileNode &fn);
|
||||
virtual void write (FileStorage &fs) const;
|
||||
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage &fs ) const;
|
||||
|
||||
protected:
|
||||
SURF surf;
|
||||
};
|
||||
|
||||
#if 0
|
||||
template<typename T>
|
||||
class CalonderDescriptorExtractor : public DescriptorExtractor
|
||||
{
|
||||
public:
|
||||
CalonderDescriptorExtractor( const string& classifierFile );
|
||||
|
||||
virtual void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
|
||||
virtual void read( const FileNode &fn );
|
||||
virtual void write( FileStorage &fs ) const;
|
||||
|
||||
protected:
|
||||
RTreeClassifier classifier_;
|
||||
static const int BORDER_SIZE = 16;
|
||||
};
|
||||
|
||||
template<typename T>
|
||||
CalonderDescriptorExtractor<T>::CalonderDescriptorExtractor(const std::string& classifier_file)
|
||||
{
|
||||
classifier_.read( classifier_file.c_str() );
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
void CalonderDescriptorExtractor<T>::compute( const cv::Mat& image,
|
||||
std::vector<cv::KeyPoint>& keypoints,
|
||||
cv::Mat& descriptors) const
|
||||
{
|
||||
// Cannot compute descriptors for keypoints on the image border.
|
||||
removeBorderKeypoints(keypoints, image.size(), BORDER_SIZE);
|
||||
|
||||
/// @todo Check 16-byte aligned
|
||||
descriptors.create(keypoints.size(), classifier_.classes(), cv::DataType<T>::type);
|
||||
IplImage ipl = (IplImage)image;
|
||||
|
||||
for (size_t i = 0; i < keypoints.size(); ++i) {
|
||||
cv::Point2f keypt = keypoints[i].pt;
|
||||
cv::WImageView1_b patch = features::extractPatch(&ipl, keypt);
|
||||
classifier_.getSignature(patch.Ipl(), descriptors.ptr<T>(i));
|
||||
}
|
||||
}
|
||||
#endif
|
||||
|
||||
CV_EXPORTS Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType );
|
||||
|
||||
/****************************************************************************************\
|
||||
@@ -1533,7 +1441,7 @@ struct CV_EXPORTS L1
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
* DMatch *
|
||||
* DMatch *
|
||||
\****************************************************************************************/
|
||||
/*
|
||||
* Struct for matching: match index and distance between descriptors
|
||||
@@ -1591,8 +1499,7 @@ public:
|
||||
* 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 ) const;
|
||||
void match( const Mat& query, const Mat& mask, vector<int>& matches ) const;
|
||||
|
||||
/*
|
||||
* Find the best match for each descriptor from a query set
|
||||
@@ -1613,8 +1520,7 @@ public:
|
||||
* mask Mask specifying permissible matches.
|
||||
* matches DMatches of the closest matches from the training set
|
||||
*/
|
||||
void match( const Mat& query, const Mat& mask,
|
||||
vector<DMatch>& matches ) const;
|
||||
void match( const Mat& query, const Mat& mask, vector<DMatch>& matches ) const;
|
||||
|
||||
/*
|
||||
* Find many matches for each descriptor from a query set
|
||||
@@ -1638,7 +1544,7 @@ public:
|
||||
* threshold Distance threshold for descriptors matching
|
||||
*/
|
||||
void match( const Mat& query, const Mat& mask,
|
||||
vector<vector<DMatch> >& matches, float threshold ) const;
|
||||
vector<vector<DMatch> >& matches, float threshold ) const;
|
||||
|
||||
|
||||
|
||||
@@ -1878,7 +1784,7 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
|
||||
|
||||
template<>
|
||||
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
|
||||
const Mat& mask, vector<int>& matches ) const;
|
||||
const Mat& mask, vector<int>& matches ) const;
|
||||
|
||||
CV_EXPORTS Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType );
|
||||
|
||||
@@ -2036,10 +1942,10 @@ public:
|
||||
virtual void clear ();
|
||||
|
||||
// Reads match object from a file node
|
||||
virtual void read (const FileNode &fn);
|
||||
virtual void read( const FileNode &fn );
|
||||
|
||||
// Writes match object to a file storage
|
||||
virtual void write (FileStorage& fs) const;
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
protected:
|
||||
Ptr<OneWayDescriptorBase> base;
|
||||
@@ -2049,6 +1955,7 @@ protected:
|
||||
/*
|
||||
* CalonderDescriptorMatch
|
||||
*/
|
||||
#if 0
|
||||
class CV_EXPORTS CalonderDescriptorMatch : public GenericDescriptorMatch
|
||||
{
|
||||
public:
|
||||
@@ -2113,6 +2020,7 @@ protected:
|
||||
Ptr<RTreeClassifier> classifier;
|
||||
Params params;
|
||||
};
|
||||
#endif
|
||||
|
||||
/*
|
||||
* FernDescriptorMatch
|
||||
@@ -2178,6 +2086,7 @@ protected:
|
||||
};
|
||||
|
||||
CV_EXPORTS Ptr<GenericDescriptorMatch> createGenericDescriptorMatch( const string& genericDescritptorMatchType, const string ¶msFilename = string () );
|
||||
|
||||
/****************************************************************************************\
|
||||
* VectorDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
@@ -2199,63 +2108,27 @@ public:
|
||||
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 );
|
||||
};
|
||||
virtual void add( const Mat& image, vector<KeyPoint>& 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 );
|
||||
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<int>& keypointIndices );
|
||||
|
||||
matcher->match( descriptors, keypointIndices );
|
||||
};
|
||||
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches );
|
||||
|
||||
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
virtual void match( const Mat& image, vector<KeyPoint>& points,
|
||||
vector<vector<DMatch> >& matches, float threshold );
|
||||
|
||||
matcher->match( descriptors, matches );
|
||||
}
|
||||
virtual void clear();
|
||||
virtual void read( const FileNode& fn );
|
||||
virtual void write( FileStorage& fs ) const;
|
||||
|
||||
virtual void match( const Mat& image, vector<KeyPoint>& points, vector<vector<DMatch> >& matches, float threshold )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, matches, threshold );
|
||||
}
|
||||
|
||||
virtual void clear()
|
||||
{
|
||||
GenericDescriptorMatch::clear();
|
||||
matcher->clear();
|
||||
}
|
||||
|
||||
virtual void read (const FileNode& fn)
|
||||
{
|
||||
GenericDescriptorMatch::read(fn);
|
||||
extractor->read (fn);
|
||||
}
|
||||
|
||||
virtual void write (FileStorage& fs) const
|
||||
{
|
||||
GenericDescriptorMatch::write(fs);
|
||||
extractor->write (fs);
|
||||
}
|
||||
protected:
|
||||
Ptr<DescriptorExtractor> extractor;
|
||||
Ptr<DescriptorMatcher> matcher;
|
||||
//vector<int> classIds;
|
||||
};
|
||||
|
||||
|
||||
struct CV_EXPORTS DrawMatchesFlags
|
||||
{
|
||||
enum{ DEFAULT = 0, // Output image matrix will be created (Mat::create),
|
||||
|
||||
+517
-959
File diff suppressed because it is too large
Load Diff
@@ -144,7 +144,7 @@ void DescriptorExtractor::removeBorderKeypoints( vector<KeyPoint>& keypoints,
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* SiftDescriptorExtractor *
|
||||
* SiftDescriptorExtractor *
|
||||
\****************************************************************************************/
|
||||
SiftDescriptorExtractor::SiftDescriptorExtractor( double magnification, bool isNormalize, bool recalculateAngles,
|
||||
int nOctaves, int nOctaveLayers, int firstOctave, int angleMode )
|
||||
@@ -188,7 +188,7 @@ void SiftDescriptorExtractor::write (FileStorage &fs) const
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* SurfDescriptorExtractor *
|
||||
* SurfDescriptorExtractor *
|
||||
\****************************************************************************************/
|
||||
SurfDescriptorExtractor::SurfDescriptorExtractor( int nOctaves,
|
||||
int nOctaveLayers, bool extended )
|
||||
@@ -228,6 +228,10 @@ void SurfDescriptorExtractor::write( FileStorage &fs ) const
|
||||
fs << "extended" << surf.extended;
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* Factory functions for descriptor extractor and matcher creating *
|
||||
\****************************************************************************************/
|
||||
|
||||
Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType )
|
||||
{
|
||||
DescriptorExtractor* de = 0;
|
||||
@@ -270,7 +274,9 @@ Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherT
|
||||
return dm;
|
||||
}
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
* BruteForceMatcher L2 specialization *
|
||||
\****************************************************************************************/
|
||||
template<>
|
||||
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
|
||||
const Mat& /*mask*/, vector<int>& matches ) const
|
||||
@@ -317,7 +323,6 @@ void BruteForceMatcher<L2<float> >::matchImpl( const Mat& descriptors_1, const M
|
||||
#endif
|
||||
}
|
||||
|
||||
|
||||
/****************************************************************************************\
|
||||
* GenericDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
@@ -394,6 +399,9 @@ void GenericDescriptorMatch::clear()
|
||||
collection.clear();
|
||||
}
|
||||
|
||||
/*
|
||||
* Factory function for GenericDescriptorMatch creating
|
||||
*/
|
||||
Ptr<GenericDescriptorMatch> createGenericDescriptorMatch( const string& genericDescritptorMatchType, const string ¶msFilename )
|
||||
{
|
||||
GenericDescriptorMatch *descriptorMatch = 0;
|
||||
@@ -409,7 +417,7 @@ Ptr<GenericDescriptorMatch> createGenericDescriptorMatch( const string& genericD
|
||||
}
|
||||
else if( ! genericDescritptorMatchType.compare ("CALONDER") )
|
||||
{
|
||||
descriptorMatch = new CalonderDescriptorMatch ();
|
||||
//descriptorMatch = new CalonderDescriptorMatch ();
|
||||
}
|
||||
|
||||
if( !paramsFilename.empty() && descriptorMatch != 0 )
|
||||
@@ -626,6 +634,7 @@ void OneWayDescriptorMatch::clear ()
|
||||
/****************************************************************************************\
|
||||
* CalonderDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
#if 0
|
||||
CalonderDescriptorMatch::Params::Params( const RNG& _rng, const PatchGenerator& _patchGen,
|
||||
int _numTrees, int _depth, int _views,
|
||||
size_t _reducedNumDim,
|
||||
@@ -774,6 +783,7 @@ void CalonderDescriptorMatch::write( FileStorage& fs ) const
|
||||
fs << "numQuantBits" << params.numQuantBits;
|
||||
fs << "printStatus" << params.printStatus;
|
||||
}
|
||||
#endif
|
||||
|
||||
/****************************************************************************************\
|
||||
* FernDescriptorMatch *
|
||||
@@ -827,22 +837,13 @@ void FernDescriptorMatch::trainFernClassifier()
|
||||
{
|
||||
assert( params.filename.empty() );
|
||||
|
||||
vector<Point2f> points;
|
||||
vector<Ptr<Mat> > refimgs;
|
||||
vector<int> labels;
|
||||
for( size_t imageIdx = 0; imageIdx < collection.images.size(); imageIdx++ )
|
||||
{
|
||||
for( size_t pointIdx = 0; pointIdx < collection.points[imageIdx].size(); pointIdx++ )
|
||||
{
|
||||
refimgs.push_back(new Mat (collection.images[imageIdx]));
|
||||
points.push_back(collection.points[imageIdx][pointIdx].pt);
|
||||
labels.push_back((int)pointIdx);
|
||||
}
|
||||
}
|
||||
vector<vector<Point2f> > points;
|
||||
for( size_t imgIdx = 0; imgIdx < collection.images.size(); imgIdx++ )
|
||||
KeyPoint::convert( collection.points[imgIdx], points[imgIdx] );
|
||||
|
||||
classifier = new FernClassifier( points, refimgs, labels, params.nclasses, params.patchSize,
|
||||
params.signatureSize, params.nstructs, params.structSize, params.nviews,
|
||||
params.compressionMethod, params.patchGenerator );
|
||||
classifier = new FernClassifier( points, collection.images, vector<vector<int> >(), 0, // each points is a class
|
||||
params.patchSize, params.signatureSize, params.nstructs, params.structSize,
|
||||
params.nviews, params.compressionMethod, params.patchGenerator );
|
||||
}
|
||||
}
|
||||
|
||||
@@ -966,4 +967,59 @@ void FernDescriptorMatch::clear ()
|
||||
classifier.release();
|
||||
}
|
||||
|
||||
/****************************************************************************************\
|
||||
* VectorDescriptorMatch *
|
||||
\****************************************************************************************/
|
||||
void VectorDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, keypoints, descriptors );
|
||||
matcher->add( descriptors );
|
||||
|
||||
collection.add( Mat(), keypoints );
|
||||
};
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<int>& keypointIndices )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, keypointIndices );
|
||||
};
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, matches );
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points,
|
||||
vector<vector<DMatch> >& matches, float threshold )
|
||||
{
|
||||
Mat descriptors;
|
||||
extractor->compute( image, points, descriptors );
|
||||
|
||||
matcher->match( descriptors, matches, threshold );
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::clear()
|
||||
{
|
||||
GenericDescriptorMatch::clear();
|
||||
matcher->clear();
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::read( const FileNode& fn )
|
||||
{
|
||||
GenericDescriptorMatch::read(fn);
|
||||
extractor->read (fn);
|
||||
}
|
||||
|
||||
void VectorDescriptorMatch::write (FileStorage& fs) const
|
||||
{
|
||||
GenericDescriptorMatch::write(fs);
|
||||
extractor->write (fs);
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@@ -692,9 +692,9 @@ Size FernClassifier::getPatchSize() const
|
||||
}
|
||||
|
||||
|
||||
FernClassifier::FernClassifier(const vector<Point2f>& points,
|
||||
const vector<Ptr<Mat> >& refimgs,
|
||||
const vector<int>& labels,
|
||||
FernClassifier::FernClassifier(const vector<vector<Point2f> >& points,
|
||||
const vector<Mat>& refimgs,
|
||||
const vector<vector<int> >& labels,
|
||||
int _nclasses, int _patchSize,
|
||||
int _signatureSize, int _nstructs,
|
||||
int _structSize, int _nviews, int _compressionMethod,
|
||||
@@ -829,41 +829,56 @@ void FernClassifier::prepare(int _nclasses, int _patchSize, int _signatureSize,
|
||||
}
|
||||
}
|
||||
|
||||
static int calcNumPoints( const vector<vector<Point2f> >& points )
|
||||
{
|
||||
int count = 0;
|
||||
for( size_t i = 0; i < points.size(); i++ )
|
||||
count += points[i].size();
|
||||
return count;
|
||||
}
|
||||
|
||||
void FernClassifier::train(const vector<Point2f>& points,
|
||||
const vector<Ptr<Mat> >& refimgs,
|
||||
const vector<int>& labels,
|
||||
void FernClassifier::train(const vector<vector<Point2f> >& points,
|
||||
const vector<Mat>& refimgs,
|
||||
const vector<vector<int> >& labels,
|
||||
int _nclasses, int _patchSize,
|
||||
int _signatureSize, int _nstructs,
|
||||
int _structSize, int _nviews, int _compressionMethod,
|
||||
const PatchGenerator& patchGenerator)
|
||||
{
|
||||
_nclasses = _nclasses > 0 ? _nclasses : (int)points.size();
|
||||
CV_Assert( points.size() == refimgs.size() );
|
||||
int numPoints = calcNumPoints( points );
|
||||
_nclasses = (!labels.empty() && _nclasses>0) ? _nclasses : numPoints;
|
||||
CV_Assert( labels.empty() || labels.size() == points.size() );
|
||||
|
||||
|
||||
prepare(_nclasses, _patchSize, _signatureSize, _nstructs,
|
||||
_structSize, _nviews, _compressionMethod);
|
||||
|
||||
// pass all the views of all the samples through the generated trees and accumulate
|
||||
// the statistics (posterior probabilities) in leaves.
|
||||
Mat patch;
|
||||
int i, j, nsamples = (int)points.size();
|
||||
RNG& rng = theRNG();
|
||||
|
||||
for( i = 0; i < nsamples; i++ )
|
||||
int globalPointIdx = 0;
|
||||
for( size_t imgIdx = 0; imgIdx < points.size(); imgIdx++ )
|
||||
{
|
||||
Point2f pt = points[i];
|
||||
const Mat& src = *refimgs[i];
|
||||
int classId = labels.empty() ? i : labels[i];
|
||||
if( verbose && (i+1)*progressBarSize/nsamples != i*progressBarSize/nsamples )
|
||||
putchar('.');
|
||||
CV_Assert( 0 <= classId && classId < nclasses );
|
||||
classCounters[classId] += _nviews;
|
||||
for( j = 0; j < _nviews; j++ )
|
||||
const Point2f* imgPoints = &points[imgIdx][0];
|
||||
const int* imgLabels = labels.empty() ? 0 : &labels[imgIdx][0];
|
||||
for( size_t pointIdx = 0; pointIdx < points[imgIdx].size(); pointIdx++, globalPointIdx++ )
|
||||
{
|
||||
patchGenerator(src, pt, patch, patchSize, rng);
|
||||
for( int f = 0; f < nstructs; f++ )
|
||||
posteriors[getLeaf(f, patch)*nclasses + classId]++;
|
||||
Point2f pt = imgPoints[pointIdx];
|
||||
const Mat& src = refimgs[imgIdx];
|
||||
int classId = imgLabels==0 ? globalPointIdx : imgLabels[pointIdx];
|
||||
if( verbose && (globalPointIdx+1)*progressBarSize/numPoints != globalPointIdx*progressBarSize/numPoints )
|
||||
putchar('.');
|
||||
CV_Assert( 0 <= classId && classId < nclasses );
|
||||
classCounters[classId] += _nviews;
|
||||
for( int v = 0; v < _nviews; v++ )
|
||||
{
|
||||
patchGenerator(src, pt, patch, patchSize, rng);
|
||||
for( int f = 0; f < nstructs; f++ )
|
||||
posteriors[getLeaf(f, patch)*nclasses + classId]++;
|
||||
}
|
||||
}
|
||||
}
|
||||
if( verbose )
|
||||
|
||||
Reference in New Issue
Block a user