tesseract 4.1.1
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cluster.cpp File Reference
#include <cfloat>
#include <cmath>
#include <vector>
#include "cluster.h"
#include "emalloc.h"
#include "genericheap.h"
#include "helpers.h"
#include "kdpair.h"
#include "matrix.h"
#include "tprintf.h"

Go to the source code of this file.

Classes

struct  TEMPCLUSTER
 
struct  STATISTICS
 
struct  BUCKETS
 
struct  CHISTRUCT
 
struct  ClusteringContext
 

Macros

#define _USE_MATH_DEFINES
 
#define HOTELLING   1
 
#define FTABLE_X   10
 
#define FTABLE_Y   100
 
#define MINVARIANCE   0.0004
 
#define MINSAMPLESPERBUCKET   5
 
#define MINSAMPLES   (MINBUCKETS * MINSAMPLESPERBUCKET)
 
#define MINSAMPLESNEEDED   1
 
#define BUCKETTABLESIZE   1024
 
#define NORMALEXTENT   3.0
 
#define Odd(N)   ((N)%2)
 
#define Mirror(N, R)   ((R) - (N) - 1)
 
#define Abs(N)   (((N) < 0) ? (-(N)) : (N))
 
#define SqrtOf2Pi   2.506628275
 
#define LOOKUPTABLESIZE   8
 
#define MAXDEGREESOFFREEDOM   MAXBUCKETS
 
#define MAXNEIGHBORS   2
 
#define MAXDISTANCE   FLT_MAX
 
#define CHIACCURACY   0.01
 
#define MINALPHA   (1e-200)
 
#define INITIALDELTA   0.1
 
#define DELTARATIO   0.1
 
#define ILLEGAL_CHAR   2
 

Typedefs

using ClusterPair = tesseract::KDPairInc< float, TEMPCLUSTER * >
 
using ClusterHeap = tesseract::GenericHeap< ClusterPair >
 
using DENSITYFUNC = double(*)(int32_t)
 
using SOLVEFUNC = double(*)(CHISTRUCT *, double)
 

Functions

CLUSTERERMakeClusterer (int16_t SampleSize, const PARAM_DESC ParamDesc[])
 
SAMPLEMakeSample (CLUSTERER *Clusterer, const float *Feature, int32_t CharID)
 
LIST ClusterSamples (CLUSTERER *Clusterer, CLUSTERCONFIG *Config)
 
void FreeClusterer (CLUSTERER *Clusterer)
 
void FreeProtoList (LIST *ProtoList)
 
void FreePrototype (void *arg)
 
CLUSTERNextSample (LIST *SearchState)
 
float Mean (PROTOTYPE *Proto, uint16_t Dimension)
 
float StandardDeviation (PROTOTYPE *Proto, uint16_t Dimension)
 
int32_t MergeClusters (int16_t N, PARAM_DESC ParamDesc[], int32_t n1, int32_t n2, float m[], float m1[], float m2[])
 

Variables

const double FTable [FTABLE_Y][FTABLE_X]
 

Macro Definition Documentation

◆ _USE_MATH_DEFINES

#define _USE_MATH_DEFINES

Definition at line 18 of file cluster.cpp.

◆ Abs

#define Abs (   N)    (((N) < 0) ? (-(N)) : (N))

Definition at line 209 of file cluster.cpp.

◆ BUCKETTABLESIZE

#define BUCKETTABLESIZE   1024

define the size of the table which maps normalized samples to histogram buckets. Also define the number of standard deviations in a normal distribution which are considered to be significant. The mapping table will be defined in such a way that it covers the specified number of standard deviations on either side of the mean. BUCKETTABLESIZE should always be even.

Definition at line 161 of file cluster.cpp.

◆ CHIACCURACY

#define CHIACCURACY   0.01

◆ DELTARATIO

#define DELTARATIO   0.1

◆ FTABLE_X

#define FTABLE_X   10

Definition at line 32 of file cluster.cpp.

◆ FTABLE_Y

#define FTABLE_Y   100

Definition at line 33 of file cluster.cpp.

◆ HOTELLING

#define HOTELLING   1

Definition at line 31 of file cluster.cpp.

◆ ILLEGAL_CHAR

#define ILLEGAL_CHAR   2

◆ INITIALDELTA

#define INITIALDELTA   0.1

◆ LOOKUPTABLESIZE

#define LOOKUPTABLESIZE   8

define lookup tables used to compute the number of histogram buckets that should be used for a given number of samples.

Definition at line 229 of file cluster.cpp.

◆ MAXDEGREESOFFREEDOM

#define MAXDEGREESOFFREEDOM   MAXBUCKETS

Definition at line 230 of file cluster.cpp.

◆ MAXDISTANCE

#define MAXDISTANCE   FLT_MAX

◆ MAXNEIGHBORS

#define MAXNEIGHBORS   2

◆ MINALPHA

#define MINALPHA   (1e-200)

◆ MINSAMPLES

#define MINSAMPLES   (MINBUCKETS * MINSAMPLESPERBUCKET)

Definition at line 152 of file cluster.cpp.

◆ MINSAMPLESNEEDED

#define MINSAMPLESNEEDED   1

Definition at line 153 of file cluster.cpp.

◆ MINSAMPLESPERBUCKET

#define MINSAMPLESPERBUCKET   5

define the absolute minimum number of samples which must be present in order to accurately test hypotheses about underlying probability distributions. Define separately the minimum samples that are needed before a statistical analysis is attempted; this number should be equal to MINSAMPLES but can be set to a lower number for early testing when very few samples are available.

Definition at line 151 of file cluster.cpp.

◆ MINVARIANCE

#define MINVARIANCE   0.0004

define the variance which will be used as a minimum variance for any dimension of any feature. Since most features are calculated from numbers with a precision no better than 1 in 128, the variance should never be less than the square of this number for parameters whose range is 1.

Definition at line 143 of file cluster.cpp.

◆ Mirror

#define Mirror (   N,
 
)    ((R) - (N) - 1)

Definition at line 208 of file cluster.cpp.

◆ NORMALEXTENT

#define NORMALEXTENT   3.0

Definition at line 162 of file cluster.cpp.

◆ Odd

#define Odd (   N)    ((N)%2)

Definition at line 207 of file cluster.cpp.

◆ SqrtOf2Pi

#define SqrtOf2Pi   2.506628275

the following variables describe a discrete normal distribution which is used by NormalDensity() and NormalBucket(). The constant NORMALEXTENT determines how many standard deviations of the distribution are mapped onto the fixed discrete range of x. x=0 is mapped to -NORMALEXTENT standard deviations and x=BUCKETTABLESIZE is mapped to +NORMALEXTENT standard deviations.

Definition at line 219 of file cluster.cpp.

Typedef Documentation

◆ ClusterHeap

Definition at line 170 of file cluster.cpp.

◆ ClusterPair

Definition at line 169 of file cluster.cpp.

◆ DENSITYFUNC

using DENSITYFUNC = double (*)(int32_t)

Definition at line 204 of file cluster.cpp.

◆ SOLVEFUNC

using SOLVEFUNC = double (*)(CHISTRUCT*, double)

Definition at line 205 of file cluster.cpp.

Function Documentation

◆ ClusterSamples()

LIST ClusterSamples ( CLUSTERER Clusterer,
CLUSTERCONFIG Config 
)

This routine first checks to see if the samples in this clusterer have already been clustered before; if so, it does not bother to recreate the cluster tree. It simply recomputes the prototypes based on the new Config info.

If the samples have not been clustered before, the samples in the KD tree are formed into a cluster tree and then the prototypes are computed from the cluster tree.

In either case this routine returns a pointer to a list of prototypes that best represent the samples given the constraints specified in Config.

Parameters
Clustererdata struct containing samples to be clustered
Configparameters which control clustering process
Returns
Pointer to a list of prototypes

Definition at line 483 of file cluster.cpp.

483 {
484 //only create cluster tree if samples have never been clustered before
485 if (Clusterer->Root == nullptr)
486 CreateClusterTree(Clusterer);
487
488 //deallocate the old prototype list if one exists
489 FreeProtoList (&Clusterer->ProtoList);
490 Clusterer->ProtoList = NIL_LIST;
491
492 //compute prototypes starting at the root node in the tree
493 ComputePrototypes(Clusterer, Config);
494 // We don't need the cluster pointers in the protos any more, so null them
495 // out, which makes it safe to delete the clusterer.
496 LIST proto_list = Clusterer->ProtoList;
497 iterate(proto_list) {
498 auto *proto = reinterpret_cast<PROTOTYPE *>(first_node(proto_list));
499 proto->Cluster = nullptr;
500 }
501 return Clusterer->ProtoList;
502} // ClusterSamples
void FreeProtoList(LIST *ProtoList)
Definition: cluster.cpp:538
#define iterate(l)
Definition: oldlist.h:101
#define first_node(l)
Definition: oldlist.h:92
#define NIL_LIST
Definition: oldlist.h:76
CLUSTERCONFIG Config
CLUSTER * Cluster
Definition: cluster.h:72
CLUSTER * Root
Definition: cluster.h:87
LIST ProtoList
Definition: cluster.h:88

◆ FreeClusterer()

void FreeClusterer ( CLUSTERER Clusterer)

This routine frees all of the memory allocated to the specified data structure. It will not, however, free the memory used by the prototype list. The pointers to the clusters for each prototype in the list will be set to nullptr to indicate that the cluster data structures no longer exist. Any sample lists that have been obtained via calls to GetSamples are no longer valid.

Parameters
Clustererpointer to data structure to be freed

Definition at line 514 of file cluster.cpp.

514 {
515 if (Clusterer != nullptr) {
516 free(Clusterer->ParamDesc);
517 if (Clusterer->KDTree != nullptr)
518 FreeKDTree (Clusterer->KDTree);
519 if (Clusterer->Root != nullptr)
520 FreeCluster (Clusterer->Root);
521 // Free up all used buckets structures.
522 for (auto & d : Clusterer->bucket_cache) {
523 for (auto & c : d)
524 if (c != nullptr)
525 FreeBuckets(c);
526 }
527
528 free(Clusterer);
529 }
530} // FreeClusterer
void FreeKDTree(KDTREE *Tree)
Definition: kdtree.cpp:331
BUCKETS * bucket_cache[DISTRIBUTION_COUNT][MAXBUCKETS+1 - MINBUCKETS]
Definition: cluster.h:91
KDTREE * KDTree
Definition: cluster.h:86
PARAM_DESC * ParamDesc
Definition: cluster.h:84

◆ FreeProtoList()

void FreeProtoList ( LIST ProtoList)

This routine frees all of the memory allocated to the specified list of prototypes. The clusters which are pointed to by the prototypes are not freed.

Parameters
ProtoListpointer to list of prototypes to be freed

Definition at line 538 of file cluster.cpp.

538 {
539 destroy_nodes(*ProtoList, FreePrototype);
540} // FreeProtoList
void FreePrototype(void *arg)
Definition: cluster.cpp:549
void destroy_nodes(LIST list, void_dest destructor)
Definition: oldlist.cpp:157

◆ FreePrototype()

void FreePrototype ( void *  arg)

This routine deallocates the memory consumed by the specified prototype and modifies the corresponding cluster so that it is no longer marked as a prototype. The cluster is NOT deallocated by this routine.

Parameters
argprototype data structure to be deallocated

Definition at line 549 of file cluster.cpp.

549 { //PROTOTYPE *Prototype)
550 auto *Prototype = static_cast<PROTOTYPE *>(arg);
551
552 // unmark the corresponding cluster (if there is one
553 if (Prototype->Cluster != nullptr)
554 Prototype->Cluster->Prototype = false;
555
556 // deallocate the prototype statistics and then the prototype itself
557 free(Prototype->Distrib);
558 free(Prototype->Mean);
559 if (Prototype->Style != spherical) {
560 free(Prototype->Variance.Elliptical);
561 free(Prototype->Magnitude.Elliptical);
562 free(Prototype->Weight.Elliptical);
563 }
564 free(Prototype);
565} // FreePrototype
@ spherical
Definition: cluster.h:44
bool Prototype
Definition: cluster.h:34

◆ MakeClusterer()

CLUSTERER * MakeClusterer ( int16_t  SampleSize,
const PARAM_DESC  ParamDesc[] 
)

This routine creates a new clusterer data structure, initializes it, and returns a pointer to it.

Parameters
SampleSizenumber of dimensions in feature space
ParamDescdescription of each dimension
Returns
pointer to the new clusterer data structure

Definition at line 376 of file cluster.cpp.

376 {
377 CLUSTERER *Clusterer;
378 int i;
379
380 // allocate main clusterer data structure and init simple fields
381 Clusterer = static_cast<CLUSTERER *>(Emalloc (sizeof (CLUSTERER)));
382 Clusterer->SampleSize = SampleSize;
383 Clusterer->NumberOfSamples = 0;
384 Clusterer->NumChar = 0;
385
386 // init fields which will not be used initially
387 Clusterer->Root = nullptr;
388 Clusterer->ProtoList = NIL_LIST;
389
390 // maintain a copy of param descriptors in the clusterer data structure
391 Clusterer->ParamDesc =
392 static_cast<PARAM_DESC *>(Emalloc (SampleSize * sizeof (PARAM_DESC)));
393 for (i = 0; i < SampleSize; i++) {
394 Clusterer->ParamDesc[i].Circular = ParamDesc[i].Circular;
395 Clusterer->ParamDesc[i].NonEssential = ParamDesc[i].NonEssential;
396 Clusterer->ParamDesc[i].Min = ParamDesc[i].Min;
397 Clusterer->ParamDesc[i].Max = ParamDesc[i].Max;
398 Clusterer->ParamDesc[i].Range = ParamDesc[i].Max - ParamDesc[i].Min;
399 Clusterer->ParamDesc[i].HalfRange = Clusterer->ParamDesc[i].Range / 2;
400 Clusterer->ParamDesc[i].MidRange =
401 (ParamDesc[i].Max + ParamDesc[i].Min) / 2;
402 }
403
404 // allocate a kd tree to hold the samples
405 Clusterer->KDTree = MakeKDTree (SampleSize, ParamDesc);
406
407 // Initialize cache of histogram buckets to minimize recomputing them.
408 for (auto & d : Clusterer->bucket_cache) {
409 for (auto & c : d)
410 c = nullptr;
411 }
412
413 return Clusterer;
414} // MakeClusterer
KDTREE * MakeKDTree(int16_t KeySize, const PARAM_DESC KeyDesc[])
Definition: kdtree.cpp:180
void * Emalloc(int Size)
Definition: emalloc.cpp:31
int32_t NumberOfSamples
Definition: cluster.h:85
int16_t SampleSize
Definition: cluster.h:83
int32_t NumChar
Definition: cluster.h:89
float HalfRange
Definition: ocrfeatures.h:48
float Range
Definition: ocrfeatures.h:47
bool Circular
Definition: ocrfeatures.h:43
float Max
Definition: ocrfeatures.h:46
float MidRange
Definition: ocrfeatures.h:49
bool NonEssential
Definition: ocrfeatures.h:44
float Min
Definition: ocrfeatures.h:45

◆ MakeSample()

SAMPLE * MakeSample ( CLUSTERER Clusterer,
const float *  Feature,
int32_t  CharID 
)

This routine creates a new sample data structure to hold the specified feature. This sample is added to the clusterer data structure (so that it knows which samples are to be clustered later), and a pointer to the sample is returned to the caller.

Parameters
Clustererclusterer data structure to add sample to
Featurefeature to be added to clusterer
CharIDunique ident. of char that sample came from
Returns
Pointer to the new sample data structure

Definition at line 429 of file cluster.cpp.

430 {
431 SAMPLE *Sample;
432 int i;
433
434 // see if the samples have already been clustered - if so trap an error
435 // Can't add samples after they have been clustered.
436 ASSERT_HOST(Clusterer->Root == nullptr);
437
438 // allocate the new sample and initialize it
439 Sample = static_cast<SAMPLE *>(Emalloc (sizeof (SAMPLE) +
440 (Clusterer->SampleSize -
441 1) * sizeof (float)));
442 Sample->Clustered = false;
443 Sample->Prototype = false;
444 Sample->SampleCount = 1;
445 Sample->Left = nullptr;
446 Sample->Right = nullptr;
447 Sample->CharID = CharID;
448
449 for (i = 0; i < Clusterer->SampleSize; i++)
450 Sample->Mean[i] = Feature[i];
451
452 // add the sample to the KD tree - keep track of the total # of samples
453 Clusterer->NumberOfSamples++;
454 KDStore(Clusterer->KDTree, Sample->Mean, Sample);
455 if (CharID >= Clusterer->NumChar)
456 Clusterer->NumChar = CharID + 1;
457
458 // execute hook for monitoring clustering operation
459 // (*SampleCreationHook)(Sample);
460
461 return (Sample);
462} // MakeSample
#define ASSERT_HOST(x)
Definition: errcode.h:88
void KDStore(KDTREE *Tree, float *Key, void *Data)
Definition: kdtree.cpp:212
Definition: cluster.h:32
struct sample * Right
Definition: cluster.h:37
int32_t CharID
Definition: cluster.h:38
unsigned SampleCount
Definition: cluster.h:35
bool Clustered
Definition: cluster.h:33
float Mean[1]
Definition: cluster.h:39
struct sample * Left
Definition: cluster.h:36

◆ Mean()

float Mean ( PROTOTYPE Proto,
uint16_t  Dimension 
)

This routine returns the mean of the specified prototype in the indicated dimension.

Parameters
Protoprototype to return mean of
Dimensiondimension whose mean is to be returned
Returns
Mean of Prototype in Dimension

Definition at line 602 of file cluster.cpp.

602 {
603 return (Proto->Mean[Dimension]);
604} // Mean
float * Mean
Definition: cluster.h:74

◆ MergeClusters()

int32_t MergeClusters ( int16_t  N,
PARAM_DESC  ParamDesc[],
int32_t  n1,
int32_t  n2,
float  m[],
float  m1[],
float  m2[] 
)

This routine merges two clusters into one larger cluster. To do this it computes the number of samples in the new cluster and the mean of the new cluster. The ParamDesc information is used to ensure that circular dimensions are handled correctly.

Parameters
N# of dimensions (size of arrays)
ParamDescarray of dimension descriptions
n1,n2number of samples in each old cluster
marray to hold mean of new cluster
m1,m2arrays containing means of old clusters
Returns
The number of samples in the new cluster.

Definition at line 824 of file cluster.cpp.

829 {
830 int32_t i, n;
831
832 n = n1 + n2;
833 for (i = N; i > 0; i--, ParamDesc++, m++, m1++, m2++) {
834 if (ParamDesc->Circular) {
835 // if distance between means is greater than allowed
836 // reduce upper point by one "rotation" to compute mean
837 // then normalize the mean back into the accepted range
838 if ((*m2 - *m1) > ParamDesc->HalfRange) {
839 *m = (n1 * *m1 + n2 * (*m2 - ParamDesc->Range)) / n;
840 if (*m < ParamDesc->Min)
841 *m += ParamDesc->Range;
842 }
843 else if ((*m1 - *m2) > ParamDesc->HalfRange) {
844 *m = (n1 * (*m1 - ParamDesc->Range) + n2 * *m2) / n;
845 if (*m < ParamDesc->Min)
846 *m += ParamDesc->Range;
847 }
848 else
849 *m = (n1 * *m1 + n2 * *m2) / n;
850 }
851 else
852 *m = (n1 * *m1 + n2 * *m2) / n;
853 }
854 return n;
855} // MergeClusters

◆ NextSample()

CLUSTER * NextSample ( LIST SearchState)

This routine is used to find all of the samples which belong to a cluster. It starts by removing the top cluster on the cluster list (SearchState). If this cluster is a leaf it is returned. Otherwise, the right subcluster is pushed on the list and we continue the search in the left subcluster. This continues until a leaf is found. If all samples have been found, nullptr is returned. InitSampleSearch() must be called before NextSample() to initialize the search.

Parameters
SearchStateptr to list containing clusters to be searched
Returns
Pointer to the next leaf cluster (sample) or nullptr.

Definition at line 580 of file cluster.cpp.

580 {
581 CLUSTER *Cluster;
582
583 if (*SearchState == NIL_LIST)
584 return (nullptr);
585 Cluster = reinterpret_cast<CLUSTER *>first_node (*SearchState);
586 *SearchState = pop (*SearchState);
587 for (;;) {
588 if (Cluster->Left == nullptr)
589 return (Cluster);
590 *SearchState = push (*SearchState, Cluster->Right);
591 Cluster = Cluster->Left;
592 }
593} // NextSample
LIST pop(LIST list)
Definition: oldlist.cpp:201
LIST push(LIST list, void *element)
Definition: oldlist.cpp:213

◆ StandardDeviation()

float StandardDeviation ( PROTOTYPE Proto,
uint16_t  Dimension 
)

This routine returns the standard deviation of the prototype in the indicated dimension.

Parameters
Protoprototype to return standard deviation of
Dimensiondimension whose stddev is to be returned
Returns
Standard deviation of Prototype in Dimension

Definition at line 613 of file cluster.cpp.

613 {
614 switch (Proto->Style) {
615 case spherical:
616 return (static_cast<float>(sqrt (static_cast<double>(Proto->Variance.Spherical))));
617 case elliptical:
618 return (static_cast<float>(sqrt (static_cast<double>(Proto->Variance.Elliptical[Dimension]))));
619 case mixed:
620 switch (Proto->Distrib[Dimension]) {
621 case normal:
622 return (static_cast<float>(sqrt (static_cast<double>(Proto->Variance.Elliptical[Dimension]))));
623 case uniform:
624 case D_random:
625 return (Proto->Variance.Elliptical[Dimension]);
627 ASSERT_HOST(!"Distribution count not allowed!");
628 }
629 }
630 return 0.0f;
631} // StandardDeviation
@ elliptical
Definition: cluster.h:44
@ mixed
Definition: cluster.h:44
@ DISTRIBUTION_COUNT
Definition: cluster.h:56
@ D_random
Definition: cluster.h:56
@ uniform
Definition: cluster.h:56
@ normal
Definition: cluster.h:56
float Spherical
Definition: cluster.h:59
float * Elliptical
Definition: cluster.h:60
FLOATUNION Variance
Definition: cluster.h:77
unsigned Style
Definition: cluster.h:70
DISTRIBUTION * Distrib
Definition: cluster.h:73

Variable Documentation

◆ FTable

const double FTable[FTABLE_Y][FTABLE_X]

Definition at line 36 of file cluster.cpp.