/****************************************************************************************\
* 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:
#endif
void read( const FileNode& fn );
+ void read( std::istream& is );
void write( FileStorage& fs ) const;
bool empty() const;
vector<uchar> quantizedPosteriors;
#endif
};
+#endif
/****************************************************************************************\
* One-Way Descriptor *
SURF surf;
};
-#if 0
template<typename T>
-class CalonderDescriptorExtractor : public DescriptorExtractor
+class CV_EXPORTS CalonderDescriptorExtractor : public DescriptorExtractor
{
public:
CalonderDescriptorExtractor( const string& classifierFile );
/// @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 );
/*
* Index the descriptors training set
*/
- void index();
+ virtual void index() = 0;
/*
* Find the best match for each descriptor from a query set
* 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 )
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 );
}
{
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++)
}
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 )
{
}
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 )
{
}
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 );
};
/*
- * 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
*/
class CV_EXPORTS FernDescriptorMatch : public GenericDescriptorMatch
//vector<int> classIds;
};
+CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
+ float maxDeltaX, float maxDeltaY );
+
struct CV_EXPORTS DrawMatchesFlags
{
#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()
{
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
+
}
projects / platform / upstream / opencv.git / commitdiff