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Commit e83c9b08 authored by Maria Dimashova's avatar Maria Dimashova
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replaced Calonder descriptor implementation; added windowedMatchingMask()

parent 4f3de6eb
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modules/features2d/include/opencv2/features2d/features2d.hpp
View file @ e83c9b08
......@@ -605,6 +605,207 @@ protected:
/****************************************************************************************\
* Calonder Classifier *
\****************************************************************************************/
struct RTreeNode;
struct CV_EXPORTS BaseKeypoint
{
int x;
int y;
IplImage* image;
BaseKeypoint()
: x(0), y(0), image(NULL)
{}
BaseKeypoint(int x, int y, IplImage* image)
: x(x), y(y), image(image)
{}
};
class CV_EXPORTS RandomizedTree
{
public:
friend class RTreeClassifier;
static const int PATCH_SIZE = 32;
static const int DEFAULT_DEPTH = 9;
static const int DEFAULT_VIEWS = 5000;
static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
static const float LOWER_QUANT_PERC = .03f;
static const float UPPER_QUANT_PERC = .92f;
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], uint8_t *dst);
// patch_data must be a 32x32 array (no row padding)
float* getPosterior(uchar* patch_data);
const float* getPosterior(uchar* patch_data) const;
uint8_t* getPosterior2(uchar* patch_data);
const uint8_t* getPosterior2(uchar* patch_data) const;
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 setKeepFloatPosteriors(bool b) { keep_float_posteriors_ = b; }
void discardFloatPosteriors() { freePosteriors(1); }
inline void applyQuantization(int num_quant_bits) { makePosteriors2(num_quant_bits); }
// debug
void savePosteriors(std::string url, bool append=false);
void savePosteriors2(std::string url, bool append=false);
private:
int classes_;
int depth_;
int num_leaves_;
std::vector<RTreeNode> nodes_;
float **posteriors_; // 16-bytes aligned posteriors
uint8_t **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 const float* getPosteriorByIndex(int index) const;
inline uint8_t* getPosteriorByIndex2(int index);
inline const uint8_t* getPosteriorByIndex2(int index) const;
//void makeRandomMeasMatrix(float *cs_phi, PHI_DISTR_TYPE dt, size_t reduced_num_dim);
void convertPosteriorsToChar();
void makePosteriors2(int num_quant_bits);
void compressLeaves(size_t reduced_num_dim);
void estimateQuantPercForPosteriors(float perc[2]);
};
inline uchar* getData(IplImage* image)
{
return reinterpret_cast<uchar*>(image->imageData);
}
inline float* RandomizedTree::getPosteriorByIndex(int index)
{
return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
}
inline const float* RandomizedTree::getPosteriorByIndex(int index) const
{
return posteriors_[index];
}
inline uint8_t* RandomizedTree::getPosteriorByIndex2(int index)
{
return const_cast<uint8_t*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex2(index));
}
inline const uint8_t* RandomizedTree::getPosteriorByIndex2(int index) const
{
return posteriors2_[index];
}
struct CV_EXPORTS RTreeNode
{
short offset1, offset2;
RTreeNode() {}
RTreeNode(uchar x1, uchar y1, uchar x2, uchar y2)
: offset1(y1*RandomizedTree::PATCH_SIZE + x1),
offset2(y2*RandomizedTree::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];
}
};
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 = 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);
void train(std::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);
// sig must point to a memory block of at least classes()*sizeof(float|uint8_t) bytes
void getSignature(IplImage *patch, uint8_t *sig) const;
void getSignature(IplImage *patch, float *sig) const;
void getSparseSignature(IplImage *patch, float *sig, float thresh) const;
// TODO: deprecated in favor of getSignature overload, remove
void getFloatSignature(IplImage *patch, float *sig) const { getSignature(patch, sig); }
static int countNonZeroElements(float *vec, int n, double tol=1e-10);
static inline void safeSignatureAlloc(uint8_t **sig, int num_sig=1, int sig_len=176);
static inline uint8_t* safeSignatureAlloc(int num_sig=1, int sig_len=176);
inline int classes() const { return classes_; }
inline int original_num_classes() const { 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;
// experimental and debug
void saveAllFloatPosteriors(std::string file_url);
void saveAllBytePosteriors(std::string file_url);
void setFloatPosteriorsFromTextfile_176(std::string url);
float countZeroElements();
std::vector<RandomizedTree> trees_;
private:
int classes_;
int num_quant_bits_;
mutable uint8_t **posteriors_;
mutable uint16_t *ptemp_;
int original_num_classes_;
bool keep_floats_;
};
#if 0
class CV_EXPORTS CalonderClassifier
{
public:
......@@ -645,6 +846,7 @@ public:
#endif
void read( const FileNode& fn );
void read( std::istream& is );
void write( FileStorage& fs ) const;
bool empty() const;
......@@ -722,6 +924,7 @@ private:
vector<uchar> quantizedPosteriors;
#endif
};
#endif
/****************************************************************************************\
* One-Way Descriptor *
......@@ -1339,9 +1542,8 @@ protected:
SURF surf;
};
#if 0
template<typename T>
class CalonderDescriptorExtractor : public DescriptorExtractor
class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const string& classifierFile );
......@@ -1371,15 +1573,23 @@ void CalonderDescriptorExtractor<T>::compute( const cv::Mat& image,
/// @todo Check 16-byte aligned
descriptors.create(keypoints.size(), classifier_.classes(), cv::DataType<T>::type);
IplImage ipl = (IplImage)image;
int patchSize = RandomizedTree::PATCH_SIZE;
int offset = patchSize / 2;
for (size_t i = 0; i < keypoints.size(); ++i) {
cv::Point2f keypt = keypoints[i].pt;
cv::WImageView1_b patch = features::extractPatch(&ipl, keypt);
classifier_.getSignature(patch.Ipl(), descriptors.ptr<T>(i));
cv::Point2f pt = keypoints[i].pt;
IplImage ipl = image( Rect(pt.x - offset, pt.y - offset, patchSize, patchSize) );
classifier_.getSignature( &ipl, descriptors.ptr<T>(i));
}
}
#endif
template<typename T>
void CalonderDescriptorExtractor<T>::read( const FileNode &fn )
{}
template<typename T>
void CalonderDescriptorExtractor<T>::write( FileStorage &fs ) const
{}
CV_EXPORTS Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType );
......@@ -1478,7 +1688,7 @@ public:
/*
* Index the descriptors training set
*/
void index();
virtual void index() = 0;
/*
* Find the best match for each descriptor from a query set
......@@ -1574,18 +1784,15 @@ protected:
* Find matches; match() calls this. Must be implemented by the subclass.
* The mask may be empty.
*/
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches ) const = 0;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<int>& matches ) const = 0;
/*
* Find matches; match() calls this. Must be implemented by the subclass.
* The mask may be empty.
*/
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<DMatch>& matches ) const = 0;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<DMatch>& matches ) const = 0;
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const = 0;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const = 0;
static bool possibleMatch( const Mat& mask, int index_1, int index_2 )
......@@ -1614,36 +1821,36 @@ inline void DescriptorMatcher::add( const Mat& descriptors )
inline void DescriptorMatcher::match( const Mat& query, vector<int>& matches ) const
{
matchImpl( query, train, Mat(), matches );
matchImpl( query, Mat(), matches );
}
inline void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<int>& matches ) const
{
matchImpl( query, train, mask, matches );
matchImpl( query, mask, matches );
}
inline void DescriptorMatcher::match( const Mat& query, vector<DMatch>& matches ) const
{
matchImpl( query, train, Mat(), matches );
matchImpl( query, Mat(), matches );
}
inline void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<DMatch>& matches ) const
{
matchImpl( query, train, mask, matches );
matchImpl( query, mask, matches );
}
inline void DescriptorMatcher::match( const Mat& query, vector<vector<DMatch> >& matches, float threshold ) const
{
matchImpl( query, train, Mat(), matches, threshold );
matchImpl( query, Mat(), matches, threshold );
}
inline void DescriptorMatcher::match( const Mat& query, const Mat& mask,
vector<vector<DMatch> >& matches, float threshold ) const
{
matchImpl( query, train, mask, matches, threshold );
matchImpl( query, mask, matches, threshold );
}
......@@ -1666,26 +1873,22 @@ class CV_EXPORTS BruteForceMatcher : public DescriptorMatcher
{
public:
BruteForceMatcher( Distance d = Distance() ) : distance(d) {}
virtual void index() {}
protected:
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches ) const;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<int>& matches ) const;
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<DMatch>& matches ) const;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<DMatch>& matches ) const;
virtual void matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const;
virtual void matchImpl( const Mat& query, const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const;
Distance distance;
};
template<class Distance> inline
void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches ) const
void BruteForceMatcher<Distance>::matchImpl( const Mat& query, const Mat& mask, vector<int>& matches ) const
{
vector<DMatch> matchings;
matchImpl( descriptors_1, descriptors_2, mask, matchings);
matchImpl( query, mask, matchings);
matches.clear();
matches.resize( matchings.size() );
for( size_t i=0;i<matchings.size();i++)
......@@ -1695,33 +1898,32 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
}
template<class Distance> inline
void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<DMatch>& matches ) const
void BruteForceMatcher<Distance>::matchImpl( const Mat& query, const Mat& mask, vector<DMatch>& matches ) const
{
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
assert( mask.empty() || (mask.rows == descriptors_1.rows && mask.cols == descriptors_2.rows) );
assert( mask.empty() || (mask.rows == query.rows && mask.cols == train.rows) );
assert( descriptors_1.cols == descriptors_2.cols || descriptors_1.empty() || descriptors_2.empty() );
assert( DataType<ValueType>::type == descriptors_1.type() || descriptors_1.empty() );
assert( DataType<ValueType>::type == descriptors_2.type() || descriptors_2.empty() );
assert( query.cols == train.cols || query.empty() || train.empty() );
assert( DataType<ValueType>::type == query.type() || query.empty() );
assert( DataType<ValueType>::type == train.type() || train.empty() );
int dimension = descriptors_1.cols;
int dimension = query.cols;
matches.clear();
matches.resize(descriptors_1.rows);
matches.resize(query.rows);
for( int i = 0; i < descriptors_1.rows; i++ )
for( int i = 0; i < query.rows; i++ )
{
const ValueType* d1 = (const ValueType*)(descriptors_1.data + descriptors_1.step*i);
const ValueType* d1 = (const ValueType*)(query.data + query.step*i);
int matchIndex = -1;
DistanceType matchDistance = std::numeric_limits<DistanceType>::max();
for( int j = 0; j < descriptors_2.rows; j++ )
for( int j = 0; j < train.rows; j++ )
{
if( possibleMatch(mask, i, j) )
{
const ValueType* d2 = (const ValueType*)(descriptors_2.data + descriptors_2.step*j);
const ValueType* d2 = (const ValueType*)(train.data + train.step*j);
DistanceType curDistance = distance(d1, d2, dimension);
if( curDistance < matchDistance )
{
......@@ -1743,31 +1945,30 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
}
template<class Distance> inline
void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const
void BruteForceMatcher<Distance>::matchImpl( const Mat& query, const Mat& mask, vector<vector<DMatch> >& matches, float threshold ) const
{
typedef typename Distance::ValueType ValueType;
typedef typename Distance::ResultType DistanceType;
assert( mask.empty() || (mask.rows == descriptors_1.rows && mask.cols == descriptors_2.rows) );
assert( mask.empty() || (mask.rows == query.rows && mask.cols == train.rows) );
assert( descriptors_1.cols == descriptors_2.cols || descriptors_1.empty() || descriptors_2.empty() );
assert( DataType<ValueType>::type == descriptors_1.type() || descriptors_1.empty() );
assert( DataType<ValueType>::type == descriptors_2.type() || descriptors_2.empty() );
assert( query.cols == train.cols || query.empty() || train.empty() );
assert( DataType<ValueType>::type == query.type() || query.empty() );
assert( DataType<ValueType>::type == train.type() || train.empty() );
int dimension = descriptors_1.cols;
int dimension = query.cols;
matches.clear();
matches.resize( descriptors_1.rows );
matches.resize( query.rows );
for( int i = 0; i < descriptors_1.rows; i++ )
for( int i = 0; i < query.rows; i++ )
{
const ValueType* d1 = (const ValueType*)(descriptors_1.data + descriptors_1.step*i);
const ValueType* d1 = (const ValueType*)(query.data + query.step*i);
for( int j = 0; j < descriptors_2.rows; j++ )
for( int j = 0; j < train.rows; j++ )
{
if( possibleMatch(mask, i, j) )
{
const ValueType* d2 = (const ValueType*)(descriptors_2.data + descriptors_2.step*j);
const ValueType* d2 = (const ValueType*)(train.data + train.step*j);
DistanceType curDistance = distance(d1, d2, dimension);
if( curDistance < threshold )
{
......@@ -1783,8 +1984,7 @@ void BruteForceMatcher<Distance>::matchImpl( const Mat& descriptors_1, const Mat
}
template<>
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& mask, vector<int>& matches ) const;
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& query, const Mat& mask, vector<int>& matches ) const;
CV_EXPORTS Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType );
......@@ -1952,76 +2152,6 @@ protected:
Params params;
};
/*
* CalonderDescriptorMatch
*/
#if 0
class CV_EXPORTS CalonderDescriptorMatch : public GenericDescriptorMatch
{
public:
class Params
{
public:
static const int DEFAULT_NUM_TREES = 80;
static const int DEFAULT_DEPTH = 9;
static const int DEFAULT_VIEWS = 5000;
static const size_t DEFAULT_REDUCED_NUM_DIM = 176;
static const size_t DEFAULT_NUM_QUANT_BITS = 4;
static const int DEFAULT_PATCH_SIZE = PATCH_SIZE;
Params( const RNG& _rng = RNG(), const PatchGenerator& _patchGen = PatchGenerator(),
int _numTrees=DEFAULT_NUM_TREES,
int _depth=DEFAULT_DEPTH,
int _views=DEFAULT_VIEWS,
size_t _reducedNumDim=DEFAULT_REDUCED_NUM_DIM,
int _numQuantBits=DEFAULT_NUM_QUANT_BITS,
bool _printStatus=true,
int _patchSize=DEFAULT_PATCH_SIZE );
Params( const string& _filename );
RNG rng;
PatchGenerator patchGen;
int numTrees;
int depth;
int views;
int patchSize;
size_t reducedNumDim;
int numQuantBits;
bool printStatus;
string filename;
};
CalonderDescriptorMatch();
CalonderDescriptorMatch( const Params& _params );
virtual ~CalonderDescriptorMatch();
void initialize( const Params& _params );
virtual void add( const Mat& image, vector<KeyPoint>& keypoints );
virtual void match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices );
virtual void classify( const Mat& image, vector<KeyPoint>& keypoints );
virtual void clear ();
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
protected:
void trainRTreeClassifier();
Mat extractPatch( const Mat& image, const Point& pt, int patchSize ) const;
void calcBestProbAndMatchIdx( const Mat& image, const Point& pt,
float& bestProb, int& bestMatchIdx, float* signature );
Ptr<RTreeClassifier> classifier;
Params params;
};
#endif
/*
* FernDescriptorMatch
*/
......@@ -2128,6 +2258,9 @@ protected:
//vector<int> classIds;
};
CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
float maxDeltaX, float maxDeltaY );
struct CV_EXPORTS DrawMatchesFlags
{
......
modules/features2d/src/calonder.cpp
View file @ e83c9b08
......@@ -42,12 +42,957 @@
#include "precomp.hpp"
#include <cstdio>
#include <iostream>
#include <fstream>
using namespace std;
const int progressBarSize = 50;
class CSMatrixGenerator {
public:
typedef enum { PDT_GAUSS=1, PDT_BERNOULLI, PDT_DBFRIENDLY } PHI_DISTR_TYPE;
~CSMatrixGenerator();
static float* getCSMatrix(int m, int n, PHI_DISTR_TYPE dt); // do NOT free returned pointer
private:
static float *cs_phi_; // matrix for compressive sensing
static int cs_phi_m_, cs_phi_n_;
};
float* CSMatrixGenerator::getCSMatrix(int m, int n, PHI_DISTR_TYPE dt)
{
assert(m <= n);
if (cs_phi_m_!=m || cs_phi_n_!=n || cs_phi_==NULL) {
if (cs_phi_) delete [] cs_phi_;
cs_phi_ = new float[m*n];
}
#if 0 // debug - load the random matrix from a file (for reproducability of results)
//assert(m == 176);
//assert(n == 500);
//const char *phi = "/u/calonder/temp/dim_red/kpca_phi.txt";
const char *phi = "/u/calonder/temp/dim_red/debug_phi.txt";
std::ifstream ifs(phi);
for (size_t i=0; i<m*n; ++i) {
if (!ifs.good()) {
printf("[ERROR] RandomizedTree::makeRandomMeasMatrix: problem reading '%s'\n", phi);
exit(0);
}
ifs >> cs_phi[i];
}
ifs.close();
static bool warned=false;
if (!warned) {
printf("[NOTE] RT: reading %ix%i PHI matrix from '%s'...\n", m, n, phi);
warned=true;
}
return;
#endif
float *cs_phi = cs_phi_;
if (m == n) {
// special case - set to 0 for safety
memset(cs_phi, 0, m*n*sizeof(float));
printf("[WARNING] %s:%i: square CS matrix (-> no reduction)\n", __FILE__, __LINE__);
}
else {
cv::RNG rng(23);
// par is distr param, cf 'Favorable JL Distributions' (Baraniuk et al, 2006)
if (dt == PDT_GAUSS) {
float par = (float)(1./m);
for (int i=0; i<m*n; ++i)
*cs_phi++ = rng.gaussian(par);
}
else if (dt == PDT_BERNOULLI) {
float par = (float)(1./sqrt(m));
for (int i=0; i<m*n; ++i)
*cs_phi++ = (rng(2)==0 ? par : -par);
}
else if (dt == PDT_DBFRIENDLY) {
float par = (float)sqrt(3./m);
for (int i=0; i<m*n; ++i) {
int i = rng(6);
*cs_phi++ = (i==0 ? par : (i==1 ? -par : 0.f));
}
}
else
throw("PHI_DISTR_TYPE not implemented.");
}
return cs_phi_;
}
CSMatrixGenerator::~CSMatrixGenerator()
{
if (cs_phi_) delete [] cs_phi_;
cs_phi_ = NULL;
}
float *CSMatrixGenerator::cs_phi_ = NULL;
int CSMatrixGenerator::cs_phi_m_ = 0;
int CSMatrixGenerator::cs_phi_n_ = 0;
inline void addVec(int size, const float* src1, const float* src2, float* dst)
{
while(--size >= 0) {
*dst = *src1 + *src2;
++dst; ++src1; ++src2;
}
}
// sum up 50 byte vectors of length 176
// assume 4 bits max for input vector values
// final shift is 2 bits right
// temp buffer should be twice as long as signature
// sig and buffer need not be initialized
inline void sum_50t_176c(uint8_t **pp, uint8_t *sig, uint16_t *temp)
{
#if CV_SSE2
__m128i acc, *acc1, *acc2, *acc3, *acc4, tzero;
__m128i *ssig, *ttemp;
ssig = (__m128i *)sig;
ttemp = (__m128i *)temp;
// empty ttemp[]
tzero = _mm_set_epi32(0, 0, 0, 0);
for (int i=0; i<22; i++)
ttemp[i] = tzero;
for (int j=0; j<48; j+=16)
{
// empty ssig[]
for (int i=0; i<11; i++)
ssig[i] = tzero;
for (int i=j; i<j+16; i+=4) // 4 columns at a time, to 16
{
acc1 = (__m128i *)pp[i];
acc2 = (__m128i *)pp[i+1];
acc3 = (__m128i *)pp[i+2];
acc4 = (__m128i *)pp[i+3];
// add next four columns
acc = _mm_adds_epu8(acc1[0],acc2[0]);
acc = _mm_adds_epu8(acc,acc3[0]);
acc = _mm_adds_epu8(acc,acc4[1]);
ssig[0] = _mm_adds_epu8(acc,ssig[0]);
// add four columns
acc = _mm_adds_epu8(acc1[1],acc2[1]);
acc = _mm_adds_epu8(acc,acc3[1]);
acc = _mm_adds_epu8(acc,acc4[1]);
ssig[1] = _mm_adds_epu8(acc,ssig[1]);
// add four columns
acc = _mm_adds_epu8(acc1[2],acc2[2]);
acc = _mm_adds_epu8(acc,acc3[2]);
acc = _mm_adds_epu8(acc,acc4[2]);
ssig[2] = _mm_adds_epu8(acc,ssig[2]);
// add four columns
acc = _mm_adds_epu8(acc1[3],acc2[3]);
acc = _mm_adds_epu8(acc,acc3[3]);
acc = _mm_adds_epu8(acc,acc4[3]);
ssig[3] = _mm_adds_epu8(acc,ssig[3]);
// add four columns
acc = _mm_adds_epu8(acc1[4],acc2[4]);
acc = _mm_adds_epu8(acc,acc3[4]);
acc = _mm_adds_epu8(acc,acc4[4]);
ssig[4] = _mm_adds_epu8(acc,ssig[4]);
// add four columns
acc = _mm_adds_epu8(acc1[5],acc2[5]);
acc = _mm_adds_epu8(acc,acc3[5]);
acc = _mm_adds_epu8(acc,acc4[5]);
ssig[5] = _mm_adds_epu8(acc,ssig[5]);
// add four columns
acc = _mm_adds_epu8(acc1[6],acc2[6]);
acc = _mm_adds_epu8(acc,acc3[6]);
acc = _mm_adds_epu8(acc,acc4[6]);
ssig[6] = _mm_adds_epu8(acc,ssig[6]);
// add four columns
acc = _mm_adds_epu8(acc1[7],acc2[7]);
acc = _mm_adds_epu8(acc,acc3[7]);
acc = _mm_adds_epu8(acc,acc4[7]);
ssig[7] = _mm_adds_epu8(acc,ssig[7]);
// add four columns
acc = _mm_adds_epu8(acc1[8],acc2[8]);
acc = _mm_adds_epu8(acc,acc3[8]);
acc = _mm_adds_epu8(acc,acc4[8]);
ssig[8] = _mm_adds_epu8(acc,ssig[8]);
// add four columns
acc = _mm_adds_epu8(acc1[9],acc2[9]);
acc = _mm_adds_epu8(acc,acc3[9]);
acc = _mm_adds_epu8(acc,acc4[9]);
ssig[9] = _mm_adds_epu8(acc,ssig[9]);
// add four columns
acc = _mm_adds_epu8(acc1[10],acc2[10]);
acc = _mm_adds_epu8(acc,acc3[10]);
acc = _mm_adds_epu8(acc,acc4[10]);
ssig[10] = _mm_adds_epu8(acc,ssig[10]);
}
// unpack to ttemp buffer and add
ttemp[0] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[0],tzero),ttemp[0]);
ttemp[1] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[0],tzero),ttemp[1]);
ttemp[2] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[1],tzero),ttemp[2]);
ttemp[3] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[1],tzero),ttemp[3]);
ttemp[4] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[2],tzero),ttemp[4]);
ttemp[5] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[2],tzero),ttemp[5]);
ttemp[6] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[3],tzero),ttemp[6]);
ttemp[7] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[3],tzero),ttemp[7]);
ttemp[8] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[4],tzero),ttemp[8]);
ttemp[9] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[4],tzero),ttemp[9]);
ttemp[10] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[5],tzero),ttemp[10]);
ttemp[11] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[5],tzero),ttemp[11]);
ttemp[12] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[6],tzero),ttemp[12]);
ttemp[13] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[6],tzero),ttemp[13]);
ttemp[14] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[7],tzero),ttemp[14]);
ttemp[15] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[7],tzero),ttemp[15]);
ttemp[16] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[8],tzero),ttemp[16]);
ttemp[17] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[8],tzero),ttemp[17]);
ttemp[18] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[9],tzero),ttemp[18]);
ttemp[19] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[9],tzero),ttemp[19]);
ttemp[20] = _mm_add_epi16(_mm_unpacklo_epi8(ssig[10],tzero),ttemp[20]);
ttemp[21] = _mm_add_epi16(_mm_unpackhi_epi8(ssig[10],tzero),ttemp[21]);
}
// create ssignature from 16-bit result
ssig[0] =_mm_packus_epi16(_mm_srai_epi16(ttemp[0],2),_mm_srai_epi16(ttemp[1],2));
ssig[1] =_mm_packus_epi16(_mm_srai_epi16(ttemp[2],2),_mm_srai_epi16(ttemp[3],2));
ssig[2] =_mm_packus_epi16(_mm_srai_epi16(ttemp[4],2),_mm_srai_epi16(ttemp[5],2));
ssig[3] =_mm_packus_epi16(_mm_srai_epi16(ttemp[6],2),_mm_srai_epi16(ttemp[7],2));
ssig[4] =_mm_packus_epi16(_mm_srai_epi16(ttemp[8],2),_mm_srai_epi16(ttemp[9],2));
ssig[5] =_mm_packus_epi16(_mm_srai_epi16(ttemp[10],2),_mm_srai_epi16(ttemp[11],2));
ssig[6] =_mm_packus_epi16(_mm_srai_epi16(ttemp[12],2),_mm_srai_epi16(ttemp[13],2));
ssig[7] =_mm_packus_epi16(_mm_srai_epi16(ttemp[14],2),_mm_srai_epi16(ttemp[15],2));
ssig[8] =_mm_packus_epi16(_mm_srai_epi16(ttemp[16],2),_mm_srai_epi16(ttemp[17],2));
ssig[9] =_mm_packus_epi16(_mm_srai_epi16(ttemp[18],2),_mm_srai_epi16(ttemp[19],2));
ssig[10] =_mm_packus_epi16(_mm_srai_epi16(ttemp[20],2),_mm_srai_epi16(ttemp[21],2));
#else
CV_Error( CV_StsNotImplemented, "Not supported without SSE2" );
#endif
}
namespace cv
{
RandomizedTree::RandomizedTree()
: posteriors_(NULL), posteriors2_(NULL)
{
}
RandomizedTree::~RandomizedTree()
{
freePosteriors(3);
}
void RandomizedTree::createNodes(int num_nodes, RNG &rng)
{
nodes_.reserve(num_nodes);
for (int i = 0; i < num_nodes; ++i) {
nodes_.push_back( RTreeNode(rng(RandomizedTree::PATCH_SIZE),
rng(RandomizedTree::PATCH_SIZE),
rng(RandomizedTree::PATCH_SIZE),
rng(RandomizedTree::PATCH_SIZE)) );
}
}
int RandomizedTree::getIndex(uchar* patch_data) const
{
int index = 0;
for (int d = 0; d < depth_; ++d) {
int child_offset = nodes_[index](patch_data);
index = 2*index + 1 + child_offset;
}
return index - nodes_.size();
}
void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
PatchGenerator make_patch;
train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits);
}
void RandomizedTree::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)
{
init(base_set.size(), depth, rng);
Mat patch;
// Estimate posterior probabilities using random affine views
std::vector<BaseKeypoint>::const_iterator keypt_it;
int class_id = 0;
Size patchSize(PATCH_SIZE, PATCH_SIZE);
for (keypt_it = base_set.begin(); keypt_it != base_set.end(); ++keypt_it, ++class_id) {
for (int i = 0; i < views; ++i) {
make_patch( Mat(keypt_it->image), Point(keypt_it->y, keypt_it->x ), patch, patchSize, rng );
IplImage iplPatch = patch;
addExample(class_id, getData(&iplPatch));
}
}
finalize(reduced_num_dim, num_quant_bits);
}
void RandomizedTree::allocPosteriorsAligned(int num_leaves, int num_classes)
{
freePosteriors(3);
posteriors_ = new float*[num_leaves]; //(float**) malloc(num_leaves*sizeof(float*));
for (int i=0; i<num_leaves; ++i) {
posteriors_[i] = (float*)cvAlloc(num_classes*sizeof(posteriors_[i][0]));
memset(posteriors_[i], 0, num_classes*sizeof(float));
}
posteriors2_ = new uint8_t*[num_leaves];
for (int i=0; i<num_leaves; ++i) {
posteriors2_[i] = (uint8_t*)cvAlloc(num_classes*sizeof(posteriors2_[i][0]));
memset(posteriors2_[i], 0, num_classes*sizeof(uint8_t));
}
classes_ = num_classes;
}
void RandomizedTree::freePosteriors(int which)
{
if (posteriors_ && (which&1)) {
for (int i=0; i<num_leaves_; ++i)
if (posteriors_[i])
cvFree( &posteriors_[i] );
delete [] posteriors_;
posteriors_ = NULL;
}
if (posteriors2_ && (which&2)) {
for (int i=0; i<num_leaves_; ++i)
cvFree( &posteriors2_[i] );
delete [] posteriors2_;
posteriors2_ = NULL;
}
classes_ = -1;
}
void RandomizedTree::init(int num_classes, int depth, RNG &rng)
{
depth_ = depth;
num_leaves_ = 1 << depth; // 2**d
int num_nodes = num_leaves_ - 1; // 2**d - 1
// Initialize probabilities and counts to 0
allocPosteriorsAligned(num_leaves_, num_classes); // will set classes_ correctly
for (int i = 0; i < num_leaves_; ++i)
memset((void*)posteriors_[i], 0, num_classes*sizeof(float));
leaf_counts_.resize(num_leaves_);
for (int i = 0; i < num_leaves_; ++i)
memset((void*)posteriors2_[i], 0, num_classes*sizeof(uint8_t));
createNodes(num_nodes, rng);
}
void RandomizedTree::addExample(int class_id, uchar* patch_data)
{
int index = getIndex(patch_data);
float* posterior = getPosteriorByIndex(index);
++leaf_counts_[index];
++posterior[class_id];
}
// returns the p% percentile of data (length n vector)
static float percentile(float *data, int n, float p)
{
assert(n>0);
assert(p>=0 && p<=1);
std::vector<float> vec(data, data+n);
sort(vec.begin(), vec.end());
int ix = (int)(p*(n-1));
return vec[ix];
}
void RandomizedTree::finalize(size_t reduced_num_dim, int num_quant_bits)
{
// Normalize by number of patches to reach each leaf
for (int index = 0; index < num_leaves_; ++index) {
float* posterior = posteriors_[index];
assert(posterior != NULL);
int count = leaf_counts_[index];
if (count != 0) {
float normalizer = 1.0f / count;
for (int c = 0; c < classes_; ++c) {
*posterior *= normalizer;
++posterior;
}
}
}
leaf_counts_.clear();
// apply compressive sensing
if ((int)reduced_num_dim != classes_)
compressLeaves(reduced_num_dim);
else {
static bool notified = false;
if (!notified)
printf("\n[OK] NO compression to leaves applied, dim=%i\n", (int)reduced_num_dim);
notified = true;
}
// convert float-posteriors to char-posteriors (quantization step)
makePosteriors2(num_quant_bits);
}
void RandomizedTree::compressLeaves(size_t reduced_num_dim)
{
static bool warned = false;
if (!warned) {
printf("\n[OK] compressing leaves with phi %i x %i\n", (int)reduced_num_dim, (int)classes_);
warned = true;
}
static bool warned2 = false;
if ((int)reduced_num_dim == classes_) {
if (!warned2)
printf("[WARNING] RandomizedTree::compressLeaves: not compressing because reduced_dim == classes()\n");
warned2 = true;
return;
}
// DO NOT FREE RETURNED POINTER
float *cs_phi = CSMatrixGenerator::getCSMatrix(reduced_num_dim, classes_, CSMatrixGenerator::PDT_BERNOULLI);
float *cs_posteriors = new float[num_leaves_ * reduced_num_dim]; // temp, num_leaves_ x reduced_num_dim
for (int i=0; i<num_leaves_; ++i) {
float *post = getPosteriorByIndex(i);
float *prod = &cs_posteriors[i*reduced_num_dim];
Mat A( reduced_num_dim, classes_, CV_32FC1, cs_phi );
Mat X( classes_, 1, CV_32FC1, post );
Mat Y( reduced_num_dim, 1, CV_32FC1, prod );
Y = A*X;
}
// copy new posteriors
freePosteriors(3);
allocPosteriorsAligned(num_leaves_, reduced_num_dim);
for (int i=0; i<num_leaves_; ++i)
memcpy(posteriors_[i], &cs_posteriors[i*reduced_num_dim], reduced_num_dim*sizeof(float));
classes_ = reduced_num_dim;
delete [] cs_posteriors;
}
void RandomizedTree::makePosteriors2(int num_quant_bits)
{
int N = (1<<num_quant_bits) - 1;
float perc[2];
estimateQuantPercForPosteriors(perc);
assert(posteriors_ != NULL);
for (int i=0; i<num_leaves_; ++i)
quantizeVector(posteriors_[i], classes_, N, perc, posteriors2_[i]);
// printf("makePosteriors2 quantization bounds: %.3e, %.3e (num_leaves=%i, N=%i)\n",
// perc[0], perc[1], num_leaves_, N);
}
void RandomizedTree::estimateQuantPercForPosteriors(float perc[2])
{
// _estimate_ percentiles for this tree
// TODO: do this more accurately
assert(posteriors_ != NULL);
perc[0] = perc[1] = .0f;
for (int i=0; i<num_leaves_; i++) {
perc[0] += percentile(posteriors_[i], classes_, LOWER_QUANT_PERC);
perc[1] += percentile(posteriors_[i], classes_, UPPER_QUANT_PERC);
}
perc[0] /= num_leaves_;
perc[1] /= num_leaves_;
}
float* RandomizedTree::getPosterior(uchar* patch_data)
{
return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosterior(patch_data));
}
const float* RandomizedTree::getPosterior(uchar* patch_data) const
{
return getPosteriorByIndex( getIndex(patch_data) );
}
uint8_t* RandomizedTree::getPosterior2(uchar* patch_data)
{
return const_cast<uint8_t*>(const_cast<const RandomizedTree*>(this)->getPosterior2(patch_data));
}
const uint8_t* RandomizedTree::getPosterior2(uchar* patch_data) const
{
return getPosteriorByIndex2( getIndex(patch_data) );
}
void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode)
{
float map_bnd[2] = {0.f,(float)N}; // bounds of quantized target interval we're mapping to
for (int k=0; k<dim; ++k, ++vec) {
*vec = float(int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]));
// 0: clamp both, lower and upper values
if (clamp_mode == 0) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : ((*vec>map_bnd[1]) ? map_bnd[1] : *vec);
// 1: clamp lower values only
else if (clamp_mode == 1) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : *vec;
// 2: clamp upper values only
else if (clamp_mode == 2) *vec = (*vec>map_bnd[1]) ? map_bnd[1] : *vec;
// 4: no clamping
else if (clamp_mode == 4) ; // yep, nothing
else {
printf("clamp_mode == %i is not valid (%s:%i).\n", clamp_mode, __FILE__, __LINE__);
exit(1);
}
}
}
void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], uint8_t *dst)
{
int map_bnd[2] = {0, N}; // bounds of quantized target interval we're mapping to
int tmp;
for (int k=0; k<dim; ++k) {
tmp = int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]);
*dst = (uint8_t)((tmp<0) ? 0 : ((tmp>N) ? N : tmp));
++vec;
++dst;
}
}
void RandomizedTree::read(const char* file_name, int num_quant_bits)
{
std::ifstream file(file_name, std::ifstream::binary);
read(file, num_quant_bits);
file.close();
}
void RandomizedTree::read(std::istream &is, int num_quant_bits)
{
is.read((char*)(&classes_), sizeof(classes_));
is.read((char*)(&depth_), sizeof(depth_));
num_leaves_ = 1 << depth_;
int num_nodes = num_leaves_ - 1;
nodes_.resize(num_nodes);
is.read((char*)(&nodes_[0]), num_nodes * sizeof(nodes_[0]));
//posteriors_.resize(classes_ * num_leaves_);
//freePosteriors(3);
//printf("[DEBUG] reading: %i leaves, %i classes\n", num_leaves_, classes_);
allocPosteriorsAligned(num_leaves_, classes_);
for (int i=0; i<num_leaves_; i++)
is.read((char*)posteriors_[i], classes_ * sizeof(*posteriors_[0]));
// make char-posteriors from float-posteriors
makePosteriors2(num_quant_bits);
}
void RandomizedTree::write(const char* file_name) const
{
std::ofstream file(file_name, std::ofstream::binary);
write(file);
file.close();
}
void RandomizedTree::write(std::ostream &os) const
{
if (!posteriors_) {
printf("WARNING: Cannot write float posteriors (posteriors_ = NULL).\n");
return;
}
os.write((char*)(&classes_), sizeof(classes_));
os.write((char*)(&depth_), sizeof(depth_));
os.write((char*)(&nodes_[0]), nodes_.size() * sizeof(nodes_[0]));
for (int i=0; i<num_leaves_; i++) {
os.write((char*)posteriors_[i], classes_ * sizeof(*posteriors_[0]));
}
}
void RandomizedTree::savePosteriors(std::string url, bool append)
{
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
for (int i=0; i<num_leaves_; i++) {
float *post = posteriors_[i];
char buf[20];
for (int i=0; i<classes_; i++) {
sprintf(buf, "%.10e", *post++);
file << buf << ((i<classes_-1) ? " " : "");
}
file << std::endl;
}
file.close();
}
void RandomizedTree::savePosteriors2(std::string url, bool append)
{
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
for (int i=0; i<num_leaves_; i++) {
uint8_t *post = posteriors2_[i];
for (int i=0; i<classes_; i++)
file << int(*post++) << (i<classes_-1?" ":"");
file << std::endl;
}
file.close();
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
RTreeClassifier::RTreeClassifier()
: classes_(0)
{
posteriors_ = NULL;
}
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, int num_trees, int depth,
int views, size_t reduced_num_dim,
int num_quant_bits)
{
PatchGenerator make_patch;
train(base_set, rng, make_patch, num_trees, depth, views, reduced_num_dim, num_quant_bits);
}
// Single-threaded version of train(), with progress output
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
RNG &rng, PatchGenerator &make_patch, int num_trees,
int depth, int views, size_t reduced_num_dim,
int num_quant_bits)
{
if (reduced_num_dim > base_set.size()) {
printf("INVALID PARAMS in RTreeClassifier::train: reduced_num_dim{%i} > base_set.size(){%i}\n",
(int)reduced_num_dim, (int)base_set.size());
return;
}
num_quant_bits_ = num_quant_bits;
classes_ = reduced_num_dim; // base_set.size();
original_num_classes_ = base_set.size();
trees_.resize(num_trees);
printf("[OK] Training trees: base size=%i, reduced size=%i\n", (int)base_set.size(), (int)reduced_num_dim);
int count = 1;
printf("[OK] Trained 0 / %i trees", num_trees); fflush(stdout);
for( int ti = 0; ti < num_trees; ti++ ) {
trees_[ti].train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
printf("\r[OK] Trained %i / %i trees", count++, num_trees);
fflush(stdout);
}
printf("\n");
countZeroElements();
printf("\n\n");
}
void RTreeClassifier::getSignature(IplImage* patch, float *sig) const
{
// Need pointer to 32x32 patch data
uchar buffer[RandomizedTree::PATCH_SIZE * RandomizedTree::PATCH_SIZE];
uchar* patch_data;
if (patch->widthStep != RandomizedTree::PATCH_SIZE) {
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
// patch->widthStep, RandomizedTree::PATCH_SIZE);
uchar* data = getData(patch);
patch_data = buffer;
for (int i = 0; i < RandomizedTree::PATCH_SIZE; ++i) {
memcpy((void*)patch_data, (void*)data, RandomizedTree::PATCH_SIZE);
data += patch->widthStep;
patch_data += RandomizedTree::PATCH_SIZE;
}
patch_data = buffer;
}
else {
patch_data = getData(patch);
}
memset((void*)sig, 0, classes_ * sizeof(float));
std::vector<RandomizedTree>::const_iterator tree_it;
// get posteriors
float **posteriors = new float*[trees_.size()]; // TODO: move alloc outside this func
float **pp = posteriors;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++) {
*pp = const_cast<float*>(tree_it->getPosterior(patch_data));
assert(*pp != NULL);
}
// sum them up
pp = posteriors;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
addVec(classes_, sig, *pp, sig);
delete [] posteriors;
posteriors = NULL;
// full quantization (experimental)
#if 0
int n_max = 1<<8 - 1;
int sum_max = (1<<4 - 1)*trees_.size();
int shift = 0;
while ((sum_max>>shift) > n_max) shift++;
for (int i = 0; i < classes_; ++i) {
sig[i] = int(sig[i] + .5) >> shift;
if (sig[i]>n_max) sig[i] = n_max;
}
static bool warned = false;
if (!warned) {
printf("[WARNING] Using full quantization (RTreeClassifier::getSignature)! shift=%i\n", shift);
warned = true;
}
#else
// TODO: get rid of this multiply (-> number of trees is known at train
// time, exploit it in RandomizedTree::finalize())
float normalizer = 1.0f / trees_.size();
for (int i = 0; i < classes_; ++i)
sig[i] *= normalizer;
#endif
}
void RTreeClassifier::getSignature(IplImage* patch, uint8_t *sig) const
{
// Need pointer to 32x32 patch data
uchar buffer[RandomizedTree::PATCH_SIZE * RandomizedTree::PATCH_SIZE];
uchar* patch_data;
if (patch->widthStep != RandomizedTree::PATCH_SIZE) {
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
// patch->widthStep, RandomizedTree::PATCH_SIZE);
uchar* data = getData(patch);
patch_data = buffer;
for (int i = 0; i < RandomizedTree::PATCH_SIZE; ++i) {
memcpy((void*)patch_data, (void*)data, RandomizedTree::PATCH_SIZE);
data += patch->widthStep;
patch_data += RandomizedTree::PATCH_SIZE;
}
patch_data = buffer;
} else {
patch_data = getData(patch);
}
std::vector<RandomizedTree>::const_iterator tree_it;
// get posteriors
if (posteriors_ == NULL)
{
posteriors_ = (uint8_t**)cvAlloc( trees_.size()*sizeof(posteriors_[0]) );
ptemp_ = (uint16_t*)cvAlloc( classes_*sizeof(ptemp_[0]) );
}
/// @todo What is going on in the next 4 lines?
uint8_t **pp = posteriors_;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
*pp = const_cast<uint8_t*>(tree_it->getPosterior2(patch_data));
pp = posteriors_;
#if 1
// SSE2 optimized code
sum_50t_176c(pp, sig, ptemp_); // sum them up
#else
static bool warned = false;
memset((void*)sig, 0, classes_ * sizeof(sig[0]));
uint16_t *sig16 = new uint16_t[classes_]; // TODO: make member, no alloc here
memset((void*)sig16, 0, classes_ * sizeof(sig16[0]));
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
addVec(classes_, sig16, *pp, sig16);
// squeeze signatures into an uint8_t
const bool full_shifting = true;
int shift;
if (full_shifting) {
float num_add_bits_f = log((float)trees_.size())/log(2.f); // # additional bits required due to summation
int num_add_bits = int(num_add_bits_f);
if (num_add_bits_f != float(num_add_bits)) ++num_add_bits;
shift = num_quant_bits_ + num_add_bits - 8*sizeof(uint8_t);
//shift = num_quant_bits_ + num_add_bits - 2;
//shift = 6;
if (shift>0)
for (int i = 0; i < classes_; ++i)
sig[i] = (sig16[i] >> shift); // &3 cut off all but lowest 2 bits, 3(dec) = 11(bin)
if (!warned)
printf("[OK] RTC: quantizing by FULL RIGHT SHIFT, shift = %i\n", shift);
}
else {
printf("[ERROR] RTC: not implemented!\n");
exit(0);
}
if (!warned)
printf("[WARNING] RTC: unoptimized signature computation\n");
warned = true;
#endif
}
void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh) const
{
getFloatSignature(patch, sig);
for (int i=0; i<classes_; ++i, sig++)
if (*sig < thresh) *sig = 0.f;
}
int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol)
{
int res = 0;
while (n-- > 0)
res += (fabs(*vec++) > tol);
return res;
}
void RTreeClassifier::read(const char* file_name)
{
std::ifstream file(file_name, std::ifstream::binary);
read(file);
file.close();
}
void RTreeClassifier::read(std::istream &is)
{
int num_trees = 0;
is.read((char*)(&num_trees), sizeof(num_trees));
is.read((char*)(&classes_), sizeof(classes_));
is.read((char*)(&original_num_classes_), sizeof(original_num_classes_));
is.read((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
if (num_quant_bits_<1 || num_quant_bits_>8) {
printf("[WARNING] RTC: suspicious value num_quant_bits_=%i found; setting to %i.\n",
num_quant_bits_, (int)DEFAULT_NUM_QUANT_BITS);
num_quant_bits_ = DEFAULT_NUM_QUANT_BITS;
}
trees_.resize(num_trees);
std::vector<RandomizedTree>::iterator tree_it;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it) {
tree_it->read(is, num_quant_bits_);
}
printf("[OK] Loaded RTC, quantization=%i bits\n", num_quant_bits_);
countZeroElements();
}
void RTreeClassifier::write(const char* file_name) const
{
std::ofstream file(file_name, std::ofstream::binary);
write(file);
file.close();
}
void RTreeClassifier::write(std::ostream &os) const
{
int num_trees = trees_.size();
os.write((char*)(&num_trees), sizeof(num_trees));
os.write((char*)(&classes_), sizeof(classes_));
os.write((char*)(&original_num_classes_), sizeof(original_num_classes_));
os.write((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
printf("RTreeClassifier::write: num_quant_bits_=%i\n", num_quant_bits_);
std::vector<RandomizedTree>::const_iterator tree_it;
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it)
tree_it->write(os);
}
void RTreeClassifier::saveAllFloatPosteriors(std::string url)
{
printf("[DEBUG] writing all float posteriors to %s...\n", url.c_str());
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].savePosteriors(url, (i==0 ? false : true));
printf("[DEBUG] done\n");
}
void RTreeClassifier::saveAllBytePosteriors(std::string url)
{
printf("[DEBUG] writing all byte posteriors to %s...\n", url.c_str());
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].savePosteriors2(url, (i==0 ? false : true));
printf("[DEBUG] done\n");
}
void RTreeClassifier::setFloatPosteriorsFromTextfile_176(std::string url)
{
std::ifstream ifs(url.c_str());
for (int i=0; i<(int)trees_.size(); ++i) {
int num_classes = trees_[i].classes_;
assert(num_classes == 176); // TODO: remove this limitation (arose due to SSE2 optimizations)
for (int k=0; k<trees_[i].num_leaves_; ++k) {
float *post = trees_[i].getPosteriorByIndex(k);
for (int j=0; j<num_classes; ++j, ++post)
ifs >> *post;
assert(ifs.good());
}
}
classes_ = 176;
//setQuantization(num_quant_bits_);
ifs.close();
printf("[EXPERIMENTAL] read entire tree from '%s'\n", url.c_str());
}
float RTreeClassifier::countZeroElements()
{
int flt_zeros = 0;
int ui8_zeros = 0;
int num_elem = trees_[0].classes();
for (int i=0; i<(int)trees_.size(); ++i)
for (int k=0; k<(int)trees_[i].num_leaves_; ++k) {
float *p = trees_[i].getPosteriorByIndex(k);
uint8_t *p2 = trees_[i].getPosteriorByIndex2(k);
assert(p); assert(p2);
for (int j=0; j<num_elem; ++j, ++p, ++p2) {
if (*p == 0.f) flt_zeros++;
if (*p2 == 0) ui8_zeros++;
}
}
num_elem = trees_.size()*trees_[0].num_leaves_*num_elem;
float flt_perc = 100.*flt_zeros/num_elem;
float ui8_perc = 100.*ui8_zeros/num_elem;
printf("[OK] RTC: overall %i/%i (%.3f%%) zeros in float leaves\n", flt_zeros, num_elem, flt_perc);
printf(" overall %i/%i (%.3f%%) zeros in uint8 leaves\n", ui8_zeros, num_elem, ui8_perc);
return flt_perc;
}
void RTreeClassifier::setQuantization(int num_quant_bits)
{
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].applyQuantization(num_quant_bits);
printf("[OK] signature quantization is now %i bits (before: %i)\n", num_quant_bits, num_quant_bits_);
num_quant_bits_ = num_quant_bits;
}
void RTreeClassifier::discardFloatPosteriors()
{
for (int i=0; i<(int)trees_.size(); ++i)
trees_[i].discardFloatPosteriors();
printf("[OK] RTC: discarded float posteriors of all trees\n");
}
#if 0
const int progressBarSize = 50;
CalonderClassifier::CalonderClassifier()
{
......@@ -561,4 +1506,81 @@ void CalonderClassifier::write( FileStorage& fs ) const
fs << "}"; // trees
}
struct RTreeNode
{
short offset1, offset2;
};
void CalonderClassifier::read( istream &is )
{
int _patchSize, _numTrees, _treeDepth, _numViews, _signatureSize, _origNumClasses, _numQuantBits, _compressType;
_patchSize = 32;
_numViews = 0;
_compressType = COMPRESS_DISTR_BERNOULLI;
is.read((char*)(&_numTrees), sizeof(_numTrees));
is.read((char*)(&_signatureSize), sizeof(_signatureSize));
is.read((char*)(&_origNumClasses), sizeof(_origNumClasses));
is.read((char*)(&_numQuantBits), sizeof(_numQuantBits));
// 1st tree
int _classes;
is.read((char*)(&_classes), sizeof(_classes));
CV_Assert( _signatureSize == _classes );
is.read((char*)(&_treeDepth), sizeof(_treeDepth));
prepare( _patchSize, _signatureSize, _numTrees, _treeDepth, _numViews );
origNumClasses = _origNumClasses;
compressType = _compressType;
if( _numQuantBits>8 )
{
if( verbose )
cout << "[WARNING] suspicious value numQuantBits=" << numQuantBits << " found; setting to " << DEFAULT_NUM_QUANT_BITS;
_numQuantBits = DEFAULT_NUM_QUANT_BITS;
}
// 1st tree
vector<RTreeNode> rtreeNodes(numNodesPerTree);
is.read((char*)(&rtreeNodes[0]), numNodesPerTree * sizeof(rtreeNodes[0]));
for( int ni = 0; ni < numNodesPerTree; ni ++ )
{
short offset1 = rtreeNodes[ni].offset1,
offset2 = rtreeNodes[ni].offset2;
nodes[ni] = Node(offset1 % _patchSize, offset1 / _patchSize, offset2 % _patchSize, offset2 / _patchSize );
}
for( int li = 0; li < numLeavesPerTree; li++ )
is.read((char*)&posteriors[li*signatureSize], signatureSize * sizeof(float));
// other trees
for( int treeIdx = 1; treeIdx < numTrees; treeIdx++ )
{
is.read((char*)(&_classes), sizeof(_classes));
CV_Assert( _classes == signatureSize );
is.read((char*)(&_treeDepth), sizeof(_treeDepth));
CV_Assert( _treeDepth == treeDepth );
is.read((char*)(&rtreeNodes[0]), numNodesPerTree * sizeof(rtreeNodes[0]));
Node* treeNodes = &nodes[treeIdx*numNodesPerTree];
for( int ni = 0; ni < numNodesPerTree; ni ++ )
{
short offset1 = rtreeNodes[ni].offset1,
offset2 = rtreeNodes[ni].offset2;
treeNodes[ni] = Node(offset1 % _patchSize, offset1 / _patchSize, offset2 % _patchSize, offset2 / _patchSize );
}
float* treePosteriors = &posteriors[treeIdx*numLeavesPerTree*signatureSize];
for( int li = 0; li < numLeavesPerTree; li++ )
is.read((char*)&treePosteriors[li*signatureSize], signatureSize * sizeof(float));
}
#if QUANTIZATION_AVAILABLE
if( _numQuantBits )
quantizePosteriors(_numQuantBits);
#endif
}
#endif
}
modules/features2d/src/descriptors.cpp
View file @ e83c9b08
......@@ -51,6 +51,24 @@ using namespace std;
namespace cv
{
Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
float maxDeltaX, float maxDeltaY )
{
if( keypoints1.empty() || keypoints2.empty() )
return Mat();
Mat mask( keypoints1.size(), keypoints2.size(), CV_8UC1 );
for( size_t i = 0; i < keypoints1.size(); i++ )
{
for( size_t j = 0; j < keypoints2.size(); j++ )
{
Point2f diff = keypoints2[j].pt - keypoints1[i].pt;
mask.at<uchar>(i, j) = std::abs(diff.x) < maxDeltaX && std::abs(diff.y) < maxDeltaY;
}
}
return mask;
}
void drawMatches( const Mat& img1, const vector<KeyPoint>& keypoints1,
const Mat& img2,const vector<KeyPoint>& keypoints2,
const vector<int>& matches, Mat& outImg,
......@@ -278,20 +296,19 @@ Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherT
* BruteForceMatcher L2 specialization *
\****************************************************************************************/
template<>
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& descriptors_1, const Mat& descriptors_2,
const Mat& /*mask*/, vector<int>& matches ) const
void BruteForceMatcher<L2<float> >::matchImpl( const Mat& query, const Mat& /*mask*/, vector<int>& matches ) const
{
matches.clear();
matches.reserve( descriptors_1.rows );
matches.reserve( query.rows );
//TODO: remove _DEBUG if bag 416 fixed
#if (defined _DEBUG || !defined HAVE_EIGEN2)
Mat norms;
cv::reduce( descriptors_2.mul( descriptors_2 ), norms, 1, 0);
cv::reduce( train.mul( train ), norms, 1, 0);
norms = norms.t();
Mat desc_2t = descriptors_2.t();
for( int i=0;i<descriptors_1.rows;i++ )
Mat desc_2t = train.t();
for( int i=0;i<query.rows;i++ )
{
Mat distances = (-2)*descriptors_1.row(i)*desc_2t;
Mat distances = (-2)*query.row(i)*desc_2t;
distances += norms;
Point minLoc;
minMaxLoc ( distances, 0, 0, &minLoc );
......@@ -631,160 +648,6 @@ void OneWayDescriptorMatch::clear ()
base->clear ();
}
/****************************************************************************************\
* CalonderDescriptorMatch *
\****************************************************************************************/
#if 0
CalonderDescriptorMatch::Params::Params( const RNG& _rng, const PatchGenerator& _patchGen,
int _numTrees, int _depth, int _views,
size_t _reducedNumDim,
int _numQuantBits,
bool _printStatus,
int _patchSize ) :
rng(_rng), patchGen(_patchGen), numTrees(_numTrees), depth(_depth), views(_views),
patchSize(_patchSize), reducedNumDim(_reducedNumDim), numQuantBits(_numQuantBits), printStatus(_printStatus)
{}
CalonderDescriptorMatch::Params::Params( const string& _filename )
{
filename = _filename;
}
CalonderDescriptorMatch::CalonderDescriptorMatch()
{}
CalonderDescriptorMatch::CalonderDescriptorMatch( const Params& _params )
{
initialize(_params);
}
CalonderDescriptorMatch::~CalonderDescriptorMatch()
{}
void CalonderDescriptorMatch::initialize( const Params& _params )
{
classifier.release();
params = _params;
if( !params.filename.empty() )
{
classifier = new RTreeClassifier;
classifier->read( params.filename.c_str() );
}
}
void CalonderDescriptorMatch::add( const Mat& image, vector<KeyPoint>& keypoints )
{
if( params.filename.empty() )
collection.add( image, keypoints );
}
Mat CalonderDescriptorMatch::extractPatch( const Mat& image, const Point& pt, int patchSize ) const
{
const int offset = patchSize / 2;
return image( Rect(pt.x - offset, pt.y - offset, patchSize, patchSize) );
}
void CalonderDescriptorMatch::calcBestProbAndMatchIdx( const Mat& image, const Point& pt,
float& bestProb, int& bestMatchIdx, float* signature )
{
IplImage roi = extractPatch( image, pt, params.patchSize );
classifier->getSignature( &roi, signature );
bestProb = 0;
bestMatchIdx = -1;
for( int ci = 0; ci < classifier->classes(); ci++ )
{
if( signature[ci] > bestProb )
{
bestProb = signature[ci];
bestMatchIdx = ci;
}
}
}
void CalonderDescriptorMatch::trainRTreeClassifier()
{
if( classifier.empty() )
{
assert( params.filename.empty() );
classifier = new RTreeClassifier;
vector<BaseKeypoint> baseKeyPoints;
vector<IplImage> iplImages( collection.images.size() );
for( size_t imageIdx = 0; imageIdx < collection.images.size(); imageIdx++ )
{
iplImages[imageIdx] = collection.images[imageIdx];
for( size_t pointIdx = 0; pointIdx < collection.points[imageIdx].size(); pointIdx++ )
{
BaseKeypoint bkp;
KeyPoint kp = collection.points[imageIdx][pointIdx];
bkp.x = cvRound(kp.pt.x);
bkp.y = cvRound(kp.pt.y);
bkp.image = &iplImages[imageIdx];
baseKeyPoints.push_back(bkp);
}
}
classifier->train( baseKeyPoints, params.rng, params.patchGen, params.numTrees,
params.depth, params.views, params.reducedNumDim, params.numQuantBits,
params.printStatus );
}
}
void CalonderDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<int>& indices )
{
trainRTreeClassifier();
float bestProb = 0;
AutoBuffer<float> signature( classifier->classes() );
indices.resize( keypoints.size() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, indices[pi], signature );
}
void CalonderDescriptorMatch::classify( const Mat& image, vector<KeyPoint>& keypoints )
{
trainRTreeClassifier();
AutoBuffer<float> signature( classifier->classes() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
{
float bestProb = 0;
int bestMatchIdx = -1;
calcBestProbAndMatchIdx( image, keypoints[pi].pt, bestProb, bestMatchIdx, signature );
keypoints[pi].class_id = collection.getKeyPoint(bestMatchIdx).class_id;
}
}
void CalonderDescriptorMatch::clear ()
{
GenericDescriptorMatch::clear();
classifier.release();
}
void CalonderDescriptorMatch::read( const FileNode &fn )
{
params.numTrees = fn["numTrees"];
params.depth = fn["depth"];
params.views = fn["views"];
params.patchSize = fn["patchSize"];
params.reducedNumDim = (int) fn["reducedNumDim"];
params.numQuantBits = fn["numQuantBits"];
params.printStatus = (int) fn["printStatus"] != 0;
}
void CalonderDescriptorMatch::write( FileStorage& fs ) const
{
fs << "numTrees" << params.numTrees;
fs << "depth" << params.depth;
fs << "views" << params.views;
fs << "patchSize" << params.patchSize;
fs << "reducedNumDim" << (int) params.reducedNumDim;
fs << "numQuantBits" << params.numQuantBits;
fs << "printStatus" << params.printStatus;
}
#endif
/****************************************************************************************\
* FernDescriptorMatch *
\****************************************************************************************/
......
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