Feature detection and description

cv::FAST

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void FAST(const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true)

Detects corners using FAST algorithm by E. Rosten (‘’Machine learning for high-speed corner detection’‘, 2006).

Parameters:
  • image – The image. Keypoints (corners) will be detected on this.
  • keypoints – Keypoints detected on the image.
  • threshold – Threshold on difference between intensity of center pixel and pixels on circle around this pixel. See description of the algorithm.
  • nonmaxSupression – If it is true then non-maximum supression will be applied to detected corners (keypoints).

MSER

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MSER

Maximally-Stable Extremal Region Extractor

class MSER : public CvMSERParams
{
public:
    // default constructor
    MSER();
    // constructor that initializes all the algorithm parameters
    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 );
    // runs the extractor on the specified image; returns the MSERs,
    // each encoded as a contour (vector<Point>, see findContours)
    // the optional mask marks the area where MSERs are searched for
    void operator()( const Mat& image, vector<vector<Point> >& msers, const Mat& mask ) const;
};

The class encapsulates all the parameters of MSER (see http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions ) extraction algorithm.

StarDetector

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StarDetector

Implements Star keypoint detector

class StarDetector : CvStarDetectorParams
{
public:
    // default constructor
    StarDetector();
    // the full constructor initialized all the algorithm parameters:
    // maxSize - maximum size of the features. The following
    //      values of the parameter are supported:
    //      4, 6, 8, 11, 12, 16, 22, 23, 32, 45, 46, 64, 90, 128
    // responseThreshold - threshold for the approximated laplacian,
    //      used to eliminate weak features. The larger it is,
    //      the less features will be retrieved
    // lineThresholdProjected - another threshold for the laplacian to
    //      eliminate edges
    // lineThresholdBinarized - another threshold for the feature
    //      size to eliminate edges.
    // The larger the 2 threshold, the more points you get.
    StarDetector(int maxSize, int responseThreshold,
                 int lineThresholdProjected,
                 int lineThresholdBinarized,
                 int suppressNonmaxSize);

    // finds keypoints in an image
    void operator()(const Mat& image, vector<KeyPoint>& keypoints) const;
};

The class implements a modified version of CenSurE keypoint detector described in Agrawal08

SIFT

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SIFT

Class for extracting keypoints and computing descriptors using approach named Scale Invariant Feature Transform (SIFT).

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();
        CommonParams( int _nOctaves, int _nOctaveLayers, int _firstOctave,
                                          int _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();
        DetectorParams( double _threshold, double _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();
        DescriptorParams( double _magnification, bool _isNormalize,
                                                  bool _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() );

    //! returns the descriptor size in floats (128)
    int descriptorSize() const { return DescriptorParams::DESCRIPTOR_SIZE; }
    //! finds the keypoints using SIFT algorithm
    void operator()(const Mat& img, const Mat& mask,
                    vector<KeyPoint>& keypoints) const;
    //! finds the keypoints and computes descriptors for them using SIFT algorithm.
    //! Optionally it can compute descriptors for the user-provided keypoints
    void operator()(const Mat& img, const Mat& mask,
                    vector<KeyPoint>& keypoints,
                    Mat& descriptors,
                    bool useProvidedKeypoints=false) const;

    CommonParams getCommonParams () const { return commParams; }
    DetectorParams getDetectorParams () const { return detectorParams; }
    DescriptorParams getDescriptorParams () const { return descriptorParams; }
protected:
    ...
};

SURF

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SURF

Class for extracting Speeded Up Robust Features from an image.

class SURF : public CvSURFParams
{
public:
    // default constructor
    SURF();
    // constructor that initializes all the algorithm parameters
    SURF(double _hessianThreshold, int _nOctaves=4,
         int _nOctaveLayers=2, bool _extended=false);
    // returns the number of elements in each descriptor (64 or 128)
    int descriptorSize() const;
    // detects keypoints using fast multi-scale Hessian detector
    void operator()(const Mat& img, const Mat& mask,
                    vector<KeyPoint>& keypoints) const;
    // detects keypoints and computes the SURF descriptors for them;
    // output vector "descriptors" stores elements of descriptors and has size
    // equal descriptorSize()*keypoints.size() as each descriptor is
    // descriptorSize() elements of this vector.
    void operator()(const Mat& img, const Mat& mask,
                    vector<KeyPoint>& keypoints,
                    vector<float>& descriptors,
                    bool useProvidedKeypoints=false) const;
};

The class SURF implements Speeded Up Robust Features descriptor Bay06 . There is fast multi-scale Hessian keypoint detector that can be used to find the keypoints (which is the default option), but the descriptors can be also computed for the user-specified keypoints. The function can be used for object tracking and localization, image stitching etc. See the find_obj.cpp demo in OpenCV samples directory.

RandomizedTree

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RandomizedTree

The class contains base structure for RTreeClassifier

class CV_EXPORTS RandomizedTree
{
public:
        friend class RTreeClassifier;

        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;

        int classes() { return classes_; }
        int depth() { return depth_; }

        void discardFloatPosteriors() { freePosteriors(1); }

        inline void applyQuantization(int num_quant_bits)
                 { makePosteriors2(num_quant_bits); }

private:
        int classes_;
        int depth_;
        int num_leaves_;
        std::vector<RTreeNode> nodes_;
        float **posteriors_;        // 16-bytes aligned posteriors
        uchar **posteriors2_;     // 16-bytes aligned posteriors
        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 convertPosteriorsToChar();
        void makePosteriors2(int num_quant_bits);
        void compressLeaves(size_t reduced_num_dim);
        void estimateQuantPercForPosteriors(float perc[2]);
};

cv::RandomizedTree::train

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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)

Trains a randomized tree using input set of keypoints

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)

{Vector of BaseKeypoint type. Contains keypoints from the image are used for training} {Random numbers generator is used for training} {Patch generator is used for training} {Maximum tree depth}

{Number of dimensions are used in compressed signature} {Number of bits are used for quantization}

cv::RandomizedTree::read

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read(const char* file_name, int num_quant_bits)

Reads pre-saved randomized tree from file or stream

read(std::istream &is, int num_quant_bits)
Parameters:
  • file_name – Filename of file contains randomized tree data
  • is – Input stream associated with file contains randomized tree data

{Number of bits are used for quantization}

cv::RandomizedTree::write

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void write(const char* file_name) const

Writes current randomized tree to a file or stream

void write(std::ostream &os) const
Parameters:
  • file_name – Filename of file where randomized tree data will be stored
  • is – Output stream associated with file where randomized tree data will be stored

cv::RandomizedTree::applyQuantization

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void applyQuantization(int num_quant_bits)

Applies quantization to the current randomized tree

{Number of bits are used for quantization}

RTreeNode

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RTreeNode

The class contains base structure for RandomizedTree

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];
        }
};

RTreeClassifier

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RTreeClassifier

The class contains RTreeClassifier . It represents calonder descriptor which was originally introduced by Michael Calonder

class CV_EXPORTS RTreeClassifier
{
public:
        static const int DEFAULT_TREES = 48;
        static const size_t DEFAULT_NUM_QUANT_BITS = 4;

        RTreeClassifier();

        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 = 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);

        // 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);

        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() { 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;

        std::vector<RandomizedTree> trees_;

private:
        int classes_;
        int num_quant_bits_;
        uchar **posteriors_;
        ushort *ptemp_;
        int original_num_classes_;
        bool keep_floats_;
};

cv::RTreeClassifier::train

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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)

Trains a randomized tree classificator using input set of keypoints

void train(std::vector<BaseKeypoint> const& base_set, cv::RNG &rng, PatchGenerator &make_patch, 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)

{Vector of BaseKeypoint type. Contains keypoints from the image are used for training} {Random numbers generator is used for training} {Patch generator is used for training} {Number of randomized trees used in RTreeClassificator} {Maximum tree depth}

{Number of dimensions are used in compressed signature} {Number of bits are used for quantization} {Print current status of training on the console}

cv::RTreeClassifier::getSignature

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void getSignature(IplImage *patch, uchar *sig)

Returns signature for image patch

void getSignature(IplImage *patch, float *sig)

{Image patch to calculate signature for} {Output signature (array dimension is reduced_num_dim) }

cv::RTreeClassifier::getSparseSignature

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void getSparseSignature(IplImage *patch, float *sig, float thresh)

The function is simular to getSignaturebut uses the threshold for removing all signature elements less than the threshold. So that the signature is compressed

{Image patch to calculate signature for} {Output signature (array dimension is reduced_num_dim) } {The threshold that is used for compressing the signature}

cv::RTreeClassifier::countNonZeroElements

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static int countNonZeroElements(float *vec, int n, double tol=1e-10)

The function returns the number of non-zero elements in the input array.

Parameters:
  • vec – Input vector contains float elements
  • n – Input vector size

{The threshold used for elements counting. We take all elements are less than tol as zero elements}

cv::RTreeClassifier::read

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read(const char* file_name)

Reads pre-saved RTreeClassifier from file or stream

read(std::istream &is)
Parameters:
  • file_name – Filename of file contains randomized tree data
  • is – Input stream associated with file contains randomized tree data

cv::RTreeClassifier::write

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void write(const char* file_name) const

Writes current RTreeClassifier to a file or stream

void write(std::ostream &os) const
Parameters:
  • file_name – Filename of file where randomized tree data will be stored
  • is – Output stream associated with file where randomized tree data will be stored

cv::RTreeClassifier::setQuantization

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void setQuantization(int num_quant_bits)

Applies quantization to the current randomized tree

{Number of bits are used for quantization}

Below there is an example of RTreeClassifier usage for feature matching. There are test and train images and we extract features from both with SURF. Output is best\_corr and best\_corr\_idx arrays which keep the best probabilities and corresponding features indexes for every train feature.

CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq *objectKeypoints = 0, *objectDescriptors = 0;
CvSeq *imageKeypoints = 0, *imageDescriptors = 0;
CvSURFParams params = cvSURFParams(500, 1);
cvExtractSURF( test_image, 0, &imageKeypoints, &imageDescriptors,
                 storage, params );
cvExtractSURF( train_image, 0, &objectKeypoints, &objectDescriptors,
                 storage, params );

cv::RTreeClassifier detector;
int patch_width = cv::PATCH_SIZE;
iint patch_height = cv::PATCH_SIZE;
vector<cv::BaseKeypoint> base_set;
int i=0;
CvSURFPoint* point;
for (i=0;i<(n_points > 0 ? n_points : objectKeypoints->total);i++)
{
        point=(CvSURFPoint*)cvGetSeqElem(objectKeypoints,i);
        base_set.push_back(
                cv::BaseKeypoint(point->pt.x,point->pt.y,train_image));
}

        //Detector training
 cv::RNG rng( cvGetTickCount() );
cv::PatchGenerator gen(0,255,2,false,0.7,1.3,-CV_PI/3,CV_PI/3,
                        -CV_PI/3,CV_PI/3);

printf("RTree Classifier training...n");
detector.train(base_set,rng,gen,24,cv::DEFAULT_DEPTH,2000,
        (int)base_set.size(), detector.DEFAULT_NUM_QUANT_BITS);
printf("Donen");

float* signature = new float[detector.original_num_classes()];
float* best_corr;
int* best_corr_idx;
if (imageKeypoints->total > 0)
{
        best_corr = new float[imageKeypoints->total];
        best_corr_idx = new int[imageKeypoints->total];
}

for(i=0; i < imageKeypoints->total; i++)
{
        point=(CvSURFPoint*)cvGetSeqElem(imageKeypoints,i);
        int part_idx = -1;
        float prob = 0.0f;

        CvRect roi = cvRect((int)(point->pt.x) - patch_width/2,
                (int)(point->pt.y) - patch_height/2,
                 patch_width, patch_height);
        cvSetImageROI(test_image, roi);
        roi = cvGetImageROI(test_image);
        if(roi.width != patch_width || roi.height != patch_height)
        {
                best_corr_idx[i] = part_idx;
                best_corr[i] = prob;
        }
        else
        {
                cvSetImageROI(test_image, roi);
                IplImage* roi_image =
                         cvCreateImage(cvSize(roi.width, roi.height),
                         test_image->depth, test_image->nChannels);
                cvCopy(test_image,roi_image);

                detector.getSignature(roi_image, signature);
                for (int j = 0; j< detector.original_num_classes();j++)
                {
                        if (prob < signature[j])
                        {
                                part_idx = j;
                                prob = signature[j];
                        }
                }

                best_corr_idx[i] = part_idx;
                best_corr[i] = prob;


                if (roi_image)
                        cvReleaseImage(&roi_image);
        }
        cvResetImageROI(test_image);
}