mirror of https://github.com/opencv/opencv.git
Open Source Computer Vision Library
https://opencv.org/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
430 lines
16 KiB
430 lines
16 KiB
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 RandomizedTree::train( vector<BaseKeypoint> const& base_set, RNG & rng, int depth, int views, size_t reduced_num_dim, int num_quant_bits ) |
|
|
|
.. ocv:function:: void RandomizedTree::train( 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:: RandomizedTree::read(const char* file_name, int num_quant_bits) |
|
|
|
.. ocv:function:: RandomizedTree::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 RandomizedTree::write(const char* file_name) const |
|
|
|
.. ocv:function:: void RandomizedTree::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 RandomizedTree::applyQuantization(int num_quant_bits) |
|
|
|
Applies quantization to the current randomized tree. |
|
|
|
:param num_quant_bits: Number of bits used for quantization. |
|
|
|
|
|
RTreeNode |
|
--------- |
|
.. ocv:struct:: 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 RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_NUM_QUANT_BITS ) |
|
|
|
.. ocv:function:: void RTreeClassifier::train( vector<BaseKeypoint> const& base_set, RNG & rng, PatchGenerator & make_patch, int num_trees=RTreeClassifier::DEFAULT_TREES, int depth=RandomizedTree::DEFAULT_DEPTH, int views=RandomizedTree::DEFAULT_VIEWS, size_t reduced_num_dim=RandomizedTree::DEFAULT_REDUCED_NUM_DIM, int num_quant_bits=DEFAULT_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 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. |
|
|
|
|
|
RTreeClassifier::getSignature |
|
--------------------------------- |
|
Returns a signature for an image patch. |
|
|
|
.. ocv:function:: void RTreeClassifier::getSignature(IplImage *patch, uchar *sig) |
|
|
|
.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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 RTreeClassifier::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:: void RTreeClassifier::read(const char* file_name) |
|
|
|
.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::write(const char* file_name) const |
|
|
|
.. ocv:function:: void RTreeClassifier::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 RTreeClassifier::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); |
|
} |
|
|
|
..
|
|
|