opencv/modules/features2d/doc/feature_detection_and_description.rst

Feature detection and description
=================================

.. highlight:: cpp

.. index:: FAST

FAST
--------
.. c:function:: 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).

    :param image: The image. Keypoints (corners) will be detected on this.

    :param keypoints: Keypoints detected on the image.

    :param threshold: Threshold on difference between intensity of center pixel and
                pixels on circle around this pixel. See description of the algorithm.

    :param nonmaxSupression: If it is true then non-maximum supression will be applied to detected corners (keypoints).

.. index:: MSER

.. _MSER:

MSER
----
.. c:type:: 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.

.. index:: StarDetector

.. _StarDetector:

StarDetector
------------
.. c:type:: 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

.. index:: SIFT

.. _SIFT:

SIFT
----
.. c:type:: 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:
        ...
    };


.. index:: SURF

.. _SURF:

SURF
----
.. c:type:: SURF

Class for extracting Speeded Up Robust Features from an image. ::

    class SURF : public CvSURFParams
    {
    public:
        // c:function::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.

.. index:: RandomizedTree

.. _RandomizedTree:

RandomizedTree
--------------
.. c:type:: 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,
                     RNG &rng, int depth, int views,
                     size_t reduced_num_dim, int num_quant_bits);
            void train(std::vector<BaseKeypoint> const& base_set,
                     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, 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, 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]);
    };

.. index:: RandomizedTree::train

RandomizedTree::train
-------------------------
.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, 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

.. c:function:: void train(std::vector<BaseKeypoint> const& base_set, 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}

.. index:: RandomizedTree::read

RandomizedTree::read
------------------------
.. c:function:: read(const char* file_name, int num_quant_bits)

    Reads pre-saved randomized tree from file or stream

.. c:function:: read(std::istream \&is, int num_quant_bits)

    :param file_name: Filename of file contains randomized tree data

    :param is: Input stream associated with file contains randomized tree data

    {Number of bits are used for quantization}

.. index:: RandomizedTree::write

RandomizedTree::write
-------------------------
.. c:function:: void write(const char* file_name) const

    Writes current randomized tree to a file or stream

.. c:function:: void write(std::ostream \&os) const

    :param file_name: Filename of file where randomized tree data will be stored

    :param is: Output stream associated with file where randomized tree data will be stored

.. index:: RandomizedTree::applyQuantization

RandomizedTree::applyQuantization
-------------------------------------
.. c:function:: void applyQuantization(int num_quant_bits)

    Applies quantization to the current randomized tree

    {Number of bits are used for quantization}

.. index:: RTreeNode

.. _RTreeNode:

RTreeNode
---------
.. c:type:: 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];
            }
    };

.. index:: RTreeClassifier

.. _RTreeClassifier:

RTreeClassifier
---------------
.. c:type:: 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,
                    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,
                    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_;
    };

.. index:: RTreeClassifier::train

RTreeClassifier::train
--------------------------
.. c:function:: void train(vector<BaseKeypoint> const& base_set, 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

.. c:function:: void train(vector<BaseKeypoint> const& base_set, 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}

.. index:: RTreeClassifier::getSignature

RTreeClassifier::getSignature
---------------------------------
.. c:function:: void getSignature(IplImage *patch, uchar *sig)

    Returns signature for image patch

.. c:function:: void getSignature(IplImage *patch, float *sig)

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

.. index:: RTreeClassifier::getSparseSignature

RTreeClassifier::getSparseSignature
--------------------------------------- 

.. c:function:: 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}

.. index:: RTreeClassifier::countNonZeroElements

RTreeClassifier::countNonZeroElements
-----------------------------------------
.. c:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)

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

    :param vec: Input vector contains float elements

    :param n: Input vector size

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

.. index:: RTreeClassifier::read

RTreeClassifier::read
-------------------------
.. c:function:: read(const char* file_name)

    Reads pre-saved RTreeClassifier from file or stream

.. c:function:: read(std::istream& is)

    :param file_name: Filename of file contains randomized tree data

    :param is: Input stream associated with file contains randomized tree data

.. index:: RTreeClassifier::write

RTreeClassifier::write
--------------------------
.. c:function:: void write(const char* file_name) const

    Writes current RTreeClassifier to a file or stream

.. c:function:: void write(std::ostream \&os) const

    :param file_name: Filename of file where randomized tree data will be stored

    :param is: Output stream associated with file where randomized tree data will be stored

.. index:: RTreeClassifier::setQuantization

RTreeClassifier::setQuantization
------------------------------------
.. c:function:: 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
:math:`best\_corr` and
:math:`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 );

    RTreeClassifier detector;
    int patch_width = PATCH_SIZE;
    iint patch_height = PATCH_SIZE;
    vector<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(
                    BaseKeypoint(point->pt.x,point->pt.y,train_image));
    }

            //Detector training
     RNG rng( cvGetTickCount() );
    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,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);
    }

..