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提交 78ef7616 编写于 作者: A Alexander Shishkov
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moved part of f2d docs to the legacy module

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modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst
浏览文件 @ 78ef7616
......@@ -183,29 +183,6 @@ Wrapping class for computing descriptors by using the
CalonderDescriptorExtractor
---------------------------
.. ocv:class:: CalonderDescriptorExtractor
Wrapping class for computing descriptors by using the
:ocv:class:`RTreeClassifier` class. ::
template<typename T>
class CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const string& classifierFile );
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
virtual int descriptorSize() const;
virtual int descriptorType() const;
protected:
...
}
OpponentColorDescriptorExtractor
--------------------------------
.. ocv:class:: OpponentColorDescriptorExtractor
......
modules/features2d/doc/common_interfaces_of_generic_descriptor_matchers.rst
浏览文件 @ 78ef7616
......@@ -238,122 +238,6 @@ Clones the matcher.
but with empty train data.
OneWayDescriptorMatcher
-----------------------
.. ocv:class:: OneWayDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`OneWayDescriptorBase` class. ::
class OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
static const int POSE_COUNT = 500;
static const int PATCH_WIDTH = 24;
static const int PATCH_HEIGHT = 24;
static float GET_MIN_SCALE() { return 0.7f; }
static float GET_MAX_SCALE() { return 1.5f; }
static float GET_STEP_SCALE() { return 1.2f; }
Params( int poseCount = POSE_COUNT,
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
string pcaFilename = string(),
string trainPath = string(), string trainImagesList = string(),
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
string pcaFilename;
string trainPath;
string trainImagesList;
float minScale, maxScale, stepScale;
};
OneWayDescriptorMatcher( const Params& params=Params() );
virtual ~OneWayDescriptorMatcher();
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
// Clears keypoints stored in collection and OneWayDescriptorBase
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
FernDescriptorMatcher
---------------------
.. ocv:class:: FernDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`FernClassifier` class. ::
class FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
Params( int nclasses=0,
int patchSize=FernClassifier::PATCH_SIZE,
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
int nstructs=FernClassifier::DEFAULT_STRUCTS,
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int nviews=FernClassifier::DEFAULT_VIEWS,
int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const string& filename );
int nclasses;
int patchSize;
int signatureSize;
int nstructs;
int structSize;
int nviews;
int compressionMethod;
PatchGenerator patchGenerator;
string filename;
};
FernDescriptorMatcher( const Params& params=Params() );
virtual ~FernDescriptorMatcher();
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
VectorDescriptorMatcher
-----------------------
.. ocv:class:: VectorDescriptorMatcher
......
modules/features2d/doc/feature_detection_and_description.rst
浏览文件 @ 78ef7616
......@@ -144,435 +144,4 @@ Class for extracting ORB features and descriptors from an image. ::
The class implements ORB.
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]);
};
RandomizedTree::train
-------------------------
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)
.. 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.
RandomizedTree::read
------------------------
Reads a pre-saved randomized tree from a file or stream.
.. ocv:function:: read(const char* file_name, int num_quant_bits)
.. ocv:function:: read(std::istream &is, int num_quant_bits)
: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.
RandomizedTree::write
-------------------------
Writes the current randomized tree to a file or stream.
.. ocv:function:: void write(const char* file_name) const
.. 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.
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.
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];
}
};
RTreeClassifier
---------------
.. ocv:class:: RTreeClassifier
Class containing ``RTreeClassifier``. It represents the Calonder descriptor 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_;
};
RTreeClassifier::train
--------------------------
Trains a randomized tree classifier using an input set of keypoints.
.. 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)
.. 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.
RTreeClassifier::getSignature
---------------------------------
Returns a signature for an image patch.
.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
.. 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)`` .
RTreeClassifier::getSparseSignature
---------------------------------------
Returns a sparse signature for an image patch
.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
:param patch: Image patch to calculate the signature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
:param thresh: Threshold used for compressing the signature.
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.
RTreeClassifier::countNonZeroElements
-----------------------------------------
Returns the number of non-zero elements in an input array.
.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
: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.
RTreeClassifier::read
-------------------------
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
.. ocv:function:: read(const char* file_name)
.. 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.
RTreeClassifier::write
--------------------------
Writes the current ``RTreeClassifier`` to a file or stream.
.. ocv:function:: void write(const char* file_name) const
.. 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.
RTreeClassifier::setQuantization
------------------------------------
Applies quantization to the current randomized tree.
.. ocv:function:: void setQuantization(int num_quant_bits)
: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);
}
..
..
\ No newline at end of file
modules/legacy/doc/common_interfaces_of_descriptor_extractors.rst 0 → 100644
浏览文件 @ 78ef7616
Common Interfaces of Descriptor Extractors
==========================================
.. highlight:: cpp
Extractors of keypoint descriptors in OpenCV have wrappers with a common interface that enables you to easily switch
between different algorithms solving the same problem. This section is devoted to computing descriptors
represented as vectors in a multidimensional space. All objects that implement the ``vector``
descriptor extractors inherit the
:ocv:class:`DescriptorExtractor` interface.
CalonderDescriptorExtractor
---------------------------
.. ocv:class:: CalonderDescriptorExtractor
Wrapping class for computing descriptors by using the
:ocv:class:`RTreeClassifier` class. ::
template<typename T>
class CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const string& classifierFile );
virtual void read( const FileNode &fn );
virtual void write( FileStorage &fs ) const;
virtual int descriptorSize() const;
virtual int descriptorType() const;
protected:
...
}
\ No newline at end of file
modules/legacy/doc/common_interfaces_of_generic_descriptor_matchers.rst 0 → 100644
浏览文件 @ 78ef7616
Common Interfaces of Generic Descriptor Matchers
================================================
.. highlight:: cpp
OneWayDescriptorMatcher
-----------------------
.. ocv:class:: OneWayDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`OneWayDescriptorBase` class. ::
class OneWayDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
static const int POSE_COUNT = 500;
static const int PATCH_WIDTH = 24;
static const int PATCH_HEIGHT = 24;
static float GET_MIN_SCALE() { return 0.7f; }
static float GET_MAX_SCALE() { return 1.5f; }
static float GET_STEP_SCALE() { return 1.2f; }
Params( int poseCount = POSE_COUNT,
Size patchSize = Size(PATCH_WIDTH, PATCH_HEIGHT),
string pcaFilename = string(),
string trainPath = string(), string trainImagesList = string(),
float minScale = GET_MIN_SCALE(), float maxScale = GET_MAX_SCALE(),
float stepScale = GET_STEP_SCALE() );
int poseCount;
Size patchSize;
string pcaFilename;
string trainPath;
string trainImagesList;
float minScale, maxScale, stepScale;
};
OneWayDescriptorMatcher( const Params& params=Params() );
virtual ~OneWayDescriptorMatcher();
void initialize( const Params& params, const Ptr<OneWayDescriptorBase>& base=Ptr<OneWayDescriptorBase>() );
// Clears keypoints stored in collection and OneWayDescriptorBase
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
FernDescriptorMatcher
---------------------
.. ocv:class:: FernDescriptorMatcher
Wrapping class for computing, matching, and classifying descriptors using the
:ocv:class:`FernClassifier` class. ::
class FernDescriptorMatcher : public GenericDescriptorMatcher
{
public:
class Params
{
public:
Params( int nclasses=0,
int patchSize=FernClassifier::PATCH_SIZE,
int signatureSize=FernClassifier::DEFAULT_SIGNATURE_SIZE,
int nstructs=FernClassifier::DEFAULT_STRUCTS,
int structSize=FernClassifier::DEFAULT_STRUCT_SIZE,
int nviews=FernClassifier::DEFAULT_VIEWS,
int compressionMethod=FernClassifier::COMPRESSION_NONE,
const PatchGenerator& patchGenerator=PatchGenerator() );
Params( const string& filename );
int nclasses;
int patchSize;
int signatureSize;
int nstructs;
int structSize;
int nviews;
int compressionMethod;
PatchGenerator patchGenerator;
string filename;
};
FernDescriptorMatcher( const Params& params=Params() );
virtual ~FernDescriptorMatcher();
virtual void clear();
virtual void train();
virtual bool isMaskSupported();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
...
};
modules/legacy/doc/feature_detection_and_description.rst 0 → 100644
浏览文件 @ 78ef7616
Feature Detection and Description
=================================
.. highlight:: cpp
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]);
};
RandomizedTree::train
-------------------------
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)
.. 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.
RandomizedTree::read
------------------------
Reads a pre-saved randomized tree from a file or stream.
.. ocv:function:: read(const char* file_name, int num_quant_bits)
.. ocv:function:: read(std::istream &is, int num_quant_bits)
: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.
RandomizedTree::write
-------------------------
Writes the current randomized tree to a file or stream.
.. ocv:function:: void write(const char* file_name) const
.. 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.
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.
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];
}
};
RTreeClassifier
---------------
.. ocv:class:: RTreeClassifier
Class containing ``RTreeClassifier``. It represents the Calonder descriptor 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_;
};
RTreeClassifier::train
--------------------------
Trains a randomized tree classifier using an input set of keypoints.
.. 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)
.. 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.
RTreeClassifier::getSignature
---------------------------------
Returns a signature for an image patch.
.. ocv:function:: void getSignature(IplImage *patch, uchar *sig)
.. 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)`` .
RTreeClassifier::getSparseSignature
---------------------------------------
Returns a sparse signature for an image patch
.. ocv:function:: void getSparseSignature(IplImage *patch, float *sig, float thresh)
:param patch: Image patch to calculate the signature for.
:param sig: Output signature (array dimension is ``reduced_num_dim)`` .
:param thresh: Threshold used for compressing the signature.
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.
RTreeClassifier::countNonZeroElements
-----------------------------------------
Returns the number of non-zero elements in an input array.
.. ocv:function:: static int countNonZeroElements(float *vec, int n, double tol=1e-10)
: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.
RTreeClassifier::read
-------------------------
Reads a pre-saved ``RTreeClassifier`` from a file or stream.
.. ocv:function:: read(const char* file_name)
.. 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.
RTreeClassifier::write
--------------------------
Writes the current ``RTreeClassifier`` to a file or stream.
.. ocv:function:: void write(const char* file_name) const
.. 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.
RTreeClassifier::setQuantization
------------------------------------
Applies quantization to the current randomized tree.
.. ocv:function:: void setQuantization(int num_quant_bits)
: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);
}
..
modules/legacy/doc/legacy.rst
浏览文件 @ 78ef7616
......@@ -10,3 +10,6 @@ legacy. Deprecated stuff
motion_analysis
expectation_maximization
planar_subdivisions
feature_detection_and_description
common_interfaces_of_descriptor_extractors
common_interfaces_of_generic_descriptor_matchers
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