mirror of
https://github.com/opencv/opencv.git
synced 2024-11-29 22:00:25 +08:00
715 lines
26 KiB
ReStructuredText
715 lines
26 KiB
ReStructuredText
Feature Detection and Description
|
|
=================================
|
|
|
|
.. highlight:: cpp
|
|
|
|
.. index:: FAST
|
|
|
|
FAST
|
|
--------
|
|
.. ocv:function:: void FAST( const Mat& image, vector<KeyPoint>& keypoints, int threshold, bool nonmaxSupression=true )
|
|
|
|
Detects corners using the FAST algorithm by E. Rosten (*Machine learning for high-speed corner detection*, 2006).
|
|
|
|
:param image: Image where keypoints (corners) are detected.
|
|
|
|
:param keypoints: Keypoints detected on the image.
|
|
|
|
:param threshold: Threshold on difference between intensity of the central pixel and pixels on a circle around this pixel. See the algorithm description below.
|
|
|
|
:param nonmaxSupression: If it is true, non-maximum supression is applied to detected corners (keypoints).
|
|
|
|
.. index:: MSER
|
|
|
|
.. _MSER:
|
|
|
|
MSER
|
|
----
|
|
.. ocv:class:: 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 the MSER extraction algorithm (see
|
|
http://en.wikipedia.org/wiki/Maximally_stable_extremal_regions).
|
|
|
|
.. index:: StarDetector
|
|
|
|
.. _StarDetector:
|
|
|
|
StarDetector
|
|
------------
|
|
.. ocv:class:: StarDetector
|
|
|
|
Class implementing the Star keypoint detector ::
|
|
|
|
class StarDetector : CvStarDetectorParams
|
|
{
|
|
public:
|
|
// default constructor
|
|
StarDetector();
|
|
// the full constructor that initializes 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 2nd 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 the ``CenSurE`` keypoint detector described in
|
|
[Agrawal08].
|
|
|
|
.. index:: SIFT
|
|
|
|
.. _SIFT:
|
|
|
|
SIFT
|
|
----
|
|
.. ocv:class:: SIFT
|
|
|
|
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) approach ::
|
|
|
|
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 the 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
|
|
----
|
|
.. ocv:class:: 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 implements the Speeded Up Robust Features descriptor
|
|
[Bay06].
|
|
There is a fast multi-scale Hessian keypoint detector that can be used to find keypoints
|
|
(default option). But the descriptors can be also computed for the user-specified keypoints.
|
|
The algorithm can be used for object tracking and localization, image stitching, and so on. See the ``find_obj.cpp`` demo in OpenCV samples directory.
|
|
|
|
|
|
.. index:: ORB
|
|
|
|
.. _ORB:
|
|
|
|
ORB
|
|
----
|
|
.. ocv:class:: ORB
|
|
|
|
Class for extracting ORB features and descriptors from an image ::
|
|
|
|
class ORB
|
|
{
|
|
public:
|
|
/** The patch sizes that can be used (only one right now) */
|
|
struct CommonParams
|
|
{
|
|
enum { DEFAULT_N_LEVELS = 3, DEFAULT_FIRST_LEVEL = 0};
|
|
|
|
/** default constructor */
|
|
CommonParams(float scale_factor = 1.2f, unsigned int n_levels = DEFAULT_N_LEVELS,
|
|
int edge_threshold = 31, unsigned int first_level = DEFAULT_FIRST_LEVEL);
|
|
void read(const FileNode& fn);
|
|
void write(FileStorage& fs) const;
|
|
|
|
/** Coefficient by which we divide the dimensions from one scale pyramid level to the next */
|
|
float scale_factor_;
|
|
/** The number of levels in the scale pyramid */
|
|
unsigned int n_levels_;
|
|
/** The level at which the image is given
|
|
* if 1, that means we will also look at the image scale_factor_ times bigger
|
|
*/
|
|
unsigned int first_level_;
|
|
/** How far from the boundary the points should be */
|
|
int edge_threshold_;
|
|
};
|
|
|
|
// c:function::default constructor
|
|
ORB();
|
|
// constructor that initializes all the algorithm parameters
|
|
ORB( const CommonParams detector_params );
|
|
// returns the number of elements in each descriptor (32 bytes)
|
|
int descriptorSize() const;
|
|
// detects keypoints using ORB
|
|
void operator()(const Mat& img, const Mat& mask,
|
|
vector<KeyPoint>& keypoints) const;
|
|
// detects ORB keypoints and computes the ORB 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,
|
|
cv::Mat& descriptors,
|
|
bool useProvidedKeypoints=false) const;
|
|
};
|
|
|
|
The class implements ORB
|
|
|
|
|
|
.. index:: RandomizedTree
|
|
|
|
.. _RandomizedTree:
|
|
|
|
RandomizedTree
|
|
--------------
|
|
.. ocv:class:: RandomizedTree
|
|
|
|
Class containing a 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);
|
|
|
|
// next two functions 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-byte aligned posteriors
|
|
uchar **posteriors2_; // 16-byte 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
|
|
-------------------------
|
|
.. ocv: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 an input set of keypoints.
|
|
|
|
.. ocv: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)
|
|
|
|
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
|
|
|
|
:param rng: Random-number generator used for training.
|
|
|
|
:param make_patch: Patch generator used for training.
|
|
|
|
:param depth: Maximum tree depth.
|
|
|
|
:param views: Number of random views of each keypoint neighborhood to generate.
|
|
|
|
:param reduced_num_dim: Number of dimensions used in the compressed signature.
|
|
|
|
:param num_quant_bits: Number of bits used for quantization.
|
|
|
|
.. index:: RandomizedTree::read
|
|
|
|
RandomizedTree::read
|
|
------------------------
|
|
.. ocv:function:: read(const char* file_name, int num_quant_bits)
|
|
|
|
.. ocv:function:: read(std::istream &is, int num_quant_bits)
|
|
|
|
Reads a pre-saved randomized tree from a file or stream.
|
|
|
|
:param file_name: Name of the file that contains randomized tree data.
|
|
|
|
:param is: Input stream associated with the file that contains randomized tree data.
|
|
|
|
:param num_quant_bits: Number of bits used for quantization.
|
|
|
|
.. index:: RandomizedTree::write
|
|
|
|
RandomizedTree::write
|
|
-------------------------
|
|
.. ocv:function:: void write(const char* file_name) const
|
|
|
|
Writes the current randomized tree to a file or stream.
|
|
|
|
.. ocv:function:: void write(std::ostream \&os) const
|
|
|
|
:param file_name: Name of the file where randomized tree data is stored.
|
|
|
|
:param is: Output stream associated with the file where randomized tree data is stored.
|
|
|
|
.. index:: RandomizedTree::applyQuantization
|
|
|
|
RandomizedTree::applyQuantization
|
|
-------------------------------------
|
|
.. ocv:function:: void applyQuantization(int num_quant_bits)
|
|
|
|
Applies quantization to the current randomized tree.
|
|
|
|
:param num_quant_bits: Number of bits used for quantization.
|
|
|
|
.. index:: RTreeNode
|
|
|
|
.. _RTreeNode:
|
|
|
|
RTreeNode
|
|
---------
|
|
.. ocv:class:: RTreeNode
|
|
|
|
Class containing a 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
|
|
---------------
|
|
.. ocv:class:: RTreeClassifier
|
|
|
|
Class containing ``RTreeClassifier``. It represents the Calonder descriptor that 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
|
|
--------------------------
|
|
.. ocv: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 classifier using an input set of keypoints.
|
|
|
|
.. ocv: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)
|
|
|
|
:param base_set: Vector of the ``BaseKeypoint`` type. It contains image keypoints used for training.
|
|
|
|
:param rng: Random-number generator used for training.
|
|
|
|
:param make_patch: Patch generator used for training.
|
|
|
|
:param num_trees: Number of randomized trees used in ``RTreeClassificator`` .
|
|
|
|
:param depth: Maximum tree depth.
|
|
|
|
:param views: Number of random views of each keypoint neighborhood to generate.
|
|
|
|
:param reduced_num_dim: Number of dimensions used in the compressed signature.
|
|
|
|
:param num_quant_bits: Number of bits used for quantization.
|
|
|
|
:param print_status: Current status of training printed on the console.
|
|
|
|
.. index:: RTreeClassifier::getSignature
|
|
|
|
RTreeClassifier::getSignature
|
|
---------------------------------
|
|
.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
|
|
|
|
Returns a signature for an image patch.
|
|
|
|
.. ocv:function:: void getSignature(IplImage *patch, float *sig)
|
|
|
|
:param patch: Image patch to calculate the signature for.
|
|
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
|
|
|
|
.. index:: RTreeClassifier::getSparseSignature
|
|
|
|
RTreeClassifier::getSparseSignature
|
|
---------------------------------------
|
|
|
|
.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
|
|
|
|
Returns a signature for an image patch similarly to ``getSignature`` but uses a threshold for removing all signature elements below the threshold so that the signature is compressed.
|
|
|
|
:param patch: Image patch to calculate the signature for.
|
|
|
|
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
|
|
|
|
:param thresh: Threshold that is used for compressing the signature.
|
|
|
|
.. index:: RTreeClassifier::countNonZeroElements
|
|
|
|
RTreeClassifier::countNonZeroElements
|
|
-----------------------------------------
|
|
.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
|
|
|
|
Returns the number of non-zero elements in an input array.
|
|
|
|
:param vec: Input vector containing float elements.
|
|
|
|
:param n: Input vector size.
|
|
|
|
:param tol: Threshold used for counting elements. All elements less than ``tol`` are considered as zero elements.
|
|
|
|
.. index:: RTreeClassifier::read
|
|
|
|
RTreeClassifier::read
|
|
-------------------------
|
|
.. ocv:function:: read(const char* file_name)
|
|
|
|
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
|
|
|
|
.. ocv:function:: read(std::istream& is)
|
|
|
|
:param file_name: Name of the file that contains randomized tree data.
|
|
|
|
:param is: Input stream associated with the file that contains randomized tree data.
|
|
|
|
.. index:: RTreeClassifier::write
|
|
|
|
RTreeClassifier::write
|
|
--------------------------
|
|
.. ocv:function:: void write(const char* file_name) const
|
|
|
|
Writes the current ``RTreeClassifier`` to a file or stream.
|
|
|
|
.. ocv:function:: void write(std::ostream &os) const
|
|
|
|
:param file_name: Name of the file where randomized tree data is stored.
|
|
|
|
:param os: Output stream associated with the file where randomized tree data is stored.
|
|
|
|
.. index:: RTreeClassifier::setQuantization
|
|
|
|
RTreeClassifier::setQuantization
|
|
------------------------------------
|
|
.. ocv:function:: void setQuantization(int num_quant_bits)
|
|
|
|
Applies quantization to the current randomized tree.
|
|
|
|
:param num_quant_bits: Number of bits used for quantization.
|
|
|
|
The example below demonstrates the usage of ``RTreeClassifier`` for matching the features. The features are extracted from the test and train images with SURF. Output is
|
|
:math:`best\_corr` and
|
|
:math:`best\_corr\_idx` arrays that keep the best probabilities and corresponding features indices 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);
|
|
}
|
|
|
|
..
|