commontraining.cpp File Reference

#include "commontraining.h"
#include "allheaders.h"
#include "ccutil.h"
#include "classify.h"
#include "cluster.h"
#include "clusttool.h"
#include "efio.h"
#include "emalloc.h"
#include "featdefs.h"
#include "fontinfo.h"
#include "freelist.h"
#include "globals.h"
#include "intfeaturespace.h"
#include "mastertrainer.h"
#include "mf.h"
#include "ndminx.h"
#include "oldlist.h"
#include "params.h"
#include "shapetable.h"
#include "tessdatamanager.h"
#include "tessopt.h"
#include "tprintf.h"
#include "unicity_table.h"
#include <math.h>

Go to the source code of this file.

Namespaces
	tesseract

Functions
	INT_PARAM_FLAG (debug_level, 0,"Level of Trainer debugging")
	INT_PARAM_FLAG (load_images, 0,"Load images with tr files")
	STRING_PARAM_FLAG (configfile,"","File to load more configs from")
	STRING_PARAM_FLAG (D,"","Directory to write output files to")
	STRING_PARAM_FLAG (F,"font_properties","File listing font properties")
	STRING_PARAM_FLAG (X,"","File listing font xheights")
	STRING_PARAM_FLAG (U,"unicharset","File to load unicharset from")
	STRING_PARAM_FLAG (O,"","File to write unicharset to")
	STRING_PARAM_FLAG (T,"","File to load trainer from")
	STRING_PARAM_FLAG (output_trainer,"","File to write trainer to")
	STRING_PARAM_FLAG (test_ch,"","UTF8 test character string")
	DOUBLE_PARAM_FLAG (clusterconfig_min_samples_fraction, Config.MinSamples,"Min number of samples per proto as % of total")
	DOUBLE_PARAM_FLAG (clusterconfig_max_illegal, Config.MaxIllegal,"Max percentage of samples in a cluster which have more"" than 1 feature in that cluster")
	DOUBLE_PARAM_FLAG (clusterconfig_independence, Config.Independence,"Desired independence between dimensions")
	DOUBLE_PARAM_FLAG (clusterconfig_confidence, Config.Confidence,"Desired confidence in prototypes created")
void	ParseArguments (int *argc, char ***argv)
ShapeTable *	tesseract::LoadShapeTable (const STRING &file_prefix)
void	tesseract::WriteShapeTable (const STRING &file_prefix, const ShapeTable &shape_table)
MasterTrainer *	tesseract::LoadTrainingData (int argc, const char *const *argv, bool replication, ShapeTable **shape_table, STRING *file_prefix)
const char *	GetNextFilename (int argc, const char *const *argv)
LABELEDLIST	FindList (LIST List, char *Label)
LABELEDLIST	NewLabeledList (const char *Label)
void	ReadTrainingSamples (const FEATURE_DEFS_STRUCT &feature_defs, const char *feature_name, int max_samples, UNICHARSET *unicharset, FILE *file, LIST *training_samples)
void	FreeTrainingSamples (LIST CharList)
void	FreeLabeledList (LABELEDLIST LabeledList)
CLUSTERER *	SetUpForClustering (const FEATURE_DEFS_STRUCT &FeatureDefs, LABELEDLIST char_sample, const char *program_feature_type)
void	MergeInsignificantProtos (LIST ProtoList, const char *label, CLUSTERER *Clusterer, CLUSTERCONFIG *Config)
void	CleanUpUnusedData (LIST ProtoList)
LIST	RemoveInsignificantProtos (LIST ProtoList, BOOL8 KeepSigProtos, BOOL8 KeepInsigProtos, int N)
MERGE_CLASS	FindClass (LIST List, const char *Label)
MERGE_CLASS	NewLabeledClass (const char *Label)
void	FreeLabeledClassList (LIST ClassList)
CLASS_STRUCT *	SetUpForFloat2Int (const UNICHARSET &unicharset, LIST LabeledClassList)
void	Normalize (float *Values)
void	FreeNormProtoList (LIST CharList)
void	AddToNormProtosList (LIST *NormProtoList, LIST ProtoList, char *CharName)
int	NumberOfProtos (LIST ProtoList, BOOL8 CountSigProtos, BOOL8 CountInsigProtos)

Variables
CLUSTERCONFIG	Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 }
FEATURE_DEFS_STRUCT	feature_defs
CCUtil	ccutil

Function Documentation

void AddToNormProtosList	(	LIST *	NormProtoList,
		LIST	ProtoList,
		char *	CharName
	)

Definition at line 854 of file commontraining.cpp.

{
  PROTOTYPE* Proto;
  LABELEDLIST LabeledProtoList;

  LabeledProtoList = NewLabeledList(CharName);
  iterate(ProtoList)
  {
    Proto = (PROTOTYPE *) first_node (ProtoList);
    LabeledProtoList->List = push(LabeledProtoList->List, Proto);
  }
  *NormProtoList = push(*NormProtoList, LabeledProtoList);
}

void CleanUpUnusedData

(

LIST

ProtoList

)

Definition at line 606 of file commontraining.cpp.

{
  PROTOTYPE* Prototype;

  iterate(ProtoList)
  {
    Prototype = (PROTOTYPE *) first_node (ProtoList);
    if(Prototype->Variance.Elliptical != NULL)
    {
      memfree(Prototype->Variance.Elliptical);
      Prototype->Variance.Elliptical = NULL;
    }
    if(Prototype->Magnitude.Elliptical != NULL)
    {
      memfree(Prototype->Magnitude.Elliptical);
      Prototype->Magnitude.Elliptical = NULL;
    }
    if(Prototype->Weight.Elliptical != NULL)
    {
      memfree(Prototype->Weight.Elliptical);
      Prototype->Weight.Elliptical = NULL;
    }
  }
}

DOUBLE_PARAM_FLAG	(	clusterconfig_min_samples_fraction	,
		Config.	MinSamples,
		"Min number of samples per proto as % of total"
	)

DOUBLE_PARAM_FLAG	(	clusterconfig_max_illegal	,
		Config.	MaxIllegal,
		"Max percentage of samples in a cluster which have more"" than 1 feature in that cluster"
	)

DOUBLE_PARAM_FLAG	(	clusterconfig_independence	,
		Config.	Independence,
		"Desired independence between dimensions"
	)

DOUBLE_PARAM_FLAG	(	clusterconfig_confidence	,
		Config.	Confidence,
		"Desired confidence in prototypes created"
	)

MERGE_CLASS FindClass	(	LIST	List,
		const char *	Label
	)

Definition at line 701 of file commontraining.cpp.

{
  MERGE_CLASS   MergeClass;

  iterate (List)
  {
    MergeClass = (MERGE_CLASS) first_node (List);
    if (strcmp (MergeClass->Label, Label) == 0)
      return (MergeClass);
  }
  return (NULL);

}       /* FindClass */

LABELEDLIST FindList	(	LIST	List,
		char *	Label
	)

This routine searches thru a list of labeled lists to find a list with the specified label. If a matching labeled list cannot be found, NULL is returned.

Parameters

List	list to search
Label	label to search for

Returns

Labeled list with the specified Label or NULL.

Note

Globals: none

Exceptions: none

History: Fri Aug 18 15:57:41 1989, DSJ, Created.

Definition at line 331 of file commontraining.cpp.

{
  LABELEDLIST   LabeledList;

  iterate (List)
  {
    LabeledList = (LABELEDLIST) first_node (List);
    if (strcmp (LabeledList->Label, Label) == 0)
      return (LabeledList);
  }
  return (NULL);

}       /* FindList */

void FreeLabeledClassList

(

LIST

ClassList

)

This routine deallocates all of the space allocated to the specified list of training samples.

Parameters

ClassList

list of all fonts in document

Returns

none

Note

Globals: none

Exceptions: none

History: Fri Aug 18 17:44:27 1989, DSJ, Created.

Definition at line 741 of file commontraining.cpp.

{
  MERGE_CLASS   MergeClass;

  iterate (ClassList)           /* iterate thru all of the fonts */
  {
    MergeClass = (MERGE_CLASS) first_node (ClassList);
    free (MergeClass->Label);
    FreeClass(MergeClass->Class);
    delete MergeClass;
  }
  destroy (ClassList);

}       /* FreeLabeledClassList */

void FreeLabeledList

(

LABELEDLIST

LabeledList

)

This routine deallocates all of the memory consumed by a labeled list. It does not free any memory which may be consumed by the items in the list.

Parameters

LabeledList

labeled list to be freed

Note

Globals: none

Returns

none

Note

Exceptions: none

History: Fri Aug 18 17:52:45 1989, DSJ, Created.

Definition at line 487 of file commontraining.cpp.

                                              {
  destroy(LabeledList->List);
  free(LabeledList->Label);
  free(LabeledList);
}  /* FreeLabeledList */

void FreeNormProtoList

(

LIST

CharList

)

Definition at line 838 of file commontraining.cpp.

{
  LABELEDLIST   char_sample;

  iterate (CharList)            /* iterate thru all of the fonts */
  {
    char_sample = (LABELEDLIST) first_node (CharList);
    FreeLabeledList (char_sample);
  }
  destroy (CharList);

}       // FreeNormProtoList

void FreeTrainingSamples

(

LIST

CharList

)

This routine deallocates all of the space allocated to the specified list of training samples.

Parameters

CharList

list of all fonts in document

Returns

none

Note

Globals: none

Exceptions: none

History: Fri Aug 18 17:44:27 1989, DSJ, Created.

Definition at line 458 of file commontraining.cpp.

                                        {
  LABELEDLIST char_sample;
  FEATURE_SET FeatureSet;
  LIST FeatureList;


  iterate(CharList) {  /* iterate thru all of the fonts */
    char_sample = (LABELEDLIST) first_node(CharList);
    FeatureList = char_sample->List;
    iterate(FeatureList) {  /* iterate thru all of the classes */
      FeatureSet = (FEATURE_SET) first_node(FeatureList);
      FreeFeatureSet(FeatureSet);
    }
    FreeLabeledList(char_sample);
  }
  destroy(CharList);
}  /* FreeTrainingSamples */

const char* GetNextFilename	(	int	argc,
		const char *const *	argv
	)

This routine returns the next command line argument. If there are no remaining command line arguments, it returns NULL. This routine should only be called after all option arguments have been parsed and removed with ParseArguments.

Globals:

• tessoptind defined by tessopt sys call

  Returns

  Next command line argument or NULL.

  Note

  Exceptions: none

  History: Fri Aug 18 09:34:12 1989, DSJ, Created.

Definition at line 310 of file commontraining.cpp.

                                                                {
  if (tessoptind < argc)
    return argv[tessoptind++];
  else
    return NULL;
}       /* GetNextFilename */

INT_PARAM_FLAG	(	debug_level	,
		0	,
		"Level of Trainer debugging"
	)

INT_PARAM_FLAG	(	load_images	,
		0	,
		"Load images with tr files"
	)

void MergeInsignificantProtos	(	LIST	ProtoList,
		const char *	label,
		CLUSTERER *	Clusterer,
		CLUSTERCONFIG *	Config
	)

Definition at line 541 of file commontraining.cpp.

                                                                           {
  PROTOTYPE     *Prototype;
  bool debug = strcmp(FLAGS_test_ch.c_str(), label) == 0;

  LIST pProtoList = ProtoList;
  iterate(pProtoList) {
    Prototype = (PROTOTYPE *) first_node (pProtoList);
    if (Prototype->Significant || Prototype->Merged)
      continue;
    FLOAT32 best_dist = 0.125;
    PROTOTYPE* best_match = NULL;
    // Find the nearest alive prototype.
    LIST list_it = ProtoList;
    iterate(list_it) {
      PROTOTYPE* test_p = (PROTOTYPE *) first_node (list_it);
      if (test_p != Prototype && !test_p->Merged) {
        FLOAT32 dist = ComputeDistance(Clusterer->SampleSize,
                                       Clusterer->ParamDesc,
                                       Prototype->Mean, test_p->Mean);
        if (dist < best_dist) {
          best_match = test_p;
          best_dist = dist;
        }
      }
    }
    if (best_match != NULL && !best_match->Significant) {
      if (debug)
        tprintf("Merging red clusters (%d+%d) at %g,%g and %g,%g\n",
                best_match->NumSamples, Prototype->NumSamples,
                best_match->Mean[0], best_match->Mean[1],
                Prototype->Mean[0], Prototype->Mean[1]);
      best_match->NumSamples = MergeClusters(Clusterer->SampleSize,
                                             Clusterer->ParamDesc,
                                             best_match->NumSamples,
                                             Prototype->NumSamples,
                                             best_match->Mean,
                                             best_match->Mean, Prototype->Mean);
      Prototype->NumSamples = 0;
      Prototype->Merged = 1;
    } else if (best_match != NULL) {
      if (debug)
        tprintf("Red proto at %g,%g matched a green one at %g,%g\n",
                Prototype->Mean[0], Prototype->Mean[1],
                best_match->Mean[0], best_match->Mean[1]);
      Prototype->Merged = 1;
    }
  }
  // Mark significant those that now have enough samples.
  int min_samples = (inT32) (Config->MinSamples * Clusterer->NumChar);
  pProtoList = ProtoList;
  iterate(pProtoList) {
    Prototype = (PROTOTYPE *) first_node (pProtoList);
    // Process insignificant protos that do not match a green one
    if (!Prototype->Significant && Prototype->NumSamples >= min_samples &&
        !Prototype->Merged) {
      if (debug)
        tprintf("Red proto at %g,%g becoming green\n",
                Prototype->Mean[0], Prototype->Mean[1]);
      Prototype->Significant = true;
    }
  }
}       /* MergeInsignificantProtos */

MERGE_CLASS NewLabeledClass

(

const char *

Label

)

Definition at line 718 of file commontraining.cpp.

{
  MERGE_CLASS   MergeClass;

  MergeClass = new MERGE_CLASS_NODE;
  MergeClass->Label = (char*)Emalloc (strlen (Label)+1);
  strcpy (MergeClass->Label, Label);
  MergeClass->Class = NewClass (MAX_NUM_PROTOS, MAX_NUM_CONFIGS);
  return (MergeClass);

}       /* NewLabeledClass */

LABELEDLIST NewLabeledList

(

const char *

Label

)

This routine allocates a new, empty labeled list and gives it the specified label.

Parameters

Label

label for new list

Returns

New, empty labeled list.

Note

Globals: none

Exceptions: none

History: Fri Aug 18 16:08:46 1989, DSJ, Created.

Definition at line 357 of file commontraining.cpp.

{
  LABELEDLIST   LabeledList;

  LabeledList = (LABELEDLIST) Emalloc (sizeof (LABELEDLISTNODE));
  LabeledList->Label = (char*)Emalloc (strlen (Label)+1);
  strcpy (LabeledList->Label, Label);
  LabeledList->List = NIL_LIST;
  LabeledList->SampleCount = 0;
  LabeledList->font_sample_count = 0;
  return (LabeledList);

}       /* NewLabeledList */

void Normalize

(

float *

Values

)

Definition at line 821 of file commontraining.cpp.

{
  register float Slope;
  register float Intercept;
  register float Normalizer;

  Slope      = tan (Values [2] * 2 * PI);
  Intercept  = Values [1] - Slope * Values [0];
  Normalizer = 1 / sqrt (Slope * Slope + 1.0);

  Values [0] = Slope * Normalizer;
  Values [1] = - Normalizer;
  Values [2] = Intercept * Normalizer;
} // Normalize

int NumberOfProtos	(	LIST	ProtoList,
		BOOL8	CountSigProtos,
		BOOL8	CountInsigProtos
	)

Definition at line 872 of file commontraining.cpp.

{
  int N = 0;
  PROTOTYPE     *Proto;

  iterate(ProtoList)
  {
    Proto = (PROTOTYPE *) first_node ( ProtoList );
    if (( Proto->Significant && CountSigProtos )        ||
        ( ! Proto->Significant && CountInsigProtos ) )
      N++;
  }
  return(N);
}

void ParseArguments	(	int *	argc,
		char ***	argv
	)

This routine parses the command line arguments that were passed to the program and ses them to set relevant training-related global parameters

Globals:

• Config current clustering parameters

  Parameters

  argc	number of command line arguments to parse
  argv	command line arguments

  Returns

  none

  Note

  Exceptions: Illegal options terminate the program.

Definition at line 88 of file commontraining.cpp.

                                             {
  STRING usage;
  if (*argc) {
    usage += (*argv)[0];
  }
  usage += " [.tr files ...]";
  tesseract::ParseCommandLineFlags(usage.c_str(), argc, argv, true);
  // Record the index of the first non-flag argument to 1, since we set
  // remove_flags to true when parsing the flags.
  tessoptind = 1;
  // Set some global values based on the flags.
  Config.MinSamples =
      MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_min_samples_fraction)));
  Config.MaxIllegal =
      MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_max_illegal)));
  Config.Independence =
      MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_independence)));
  Config.Confidence =
      MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_confidence)));
  // Set additional parameters from config file if specified.
  if (!FLAGS_configfile.empty()) {
    tesseract::ParamUtils::ReadParamsFile(
        FLAGS_configfile.c_str(),
        tesseract::SET_PARAM_CONSTRAINT_NON_INIT_ONLY,
        ccutil.params());
  }
}

void ReadTrainingSamples	(	const FEATURE_DEFS_STRUCT &	feature_defs,
		const char *	feature_name,
		int	max_samples,
		UNICHARSET *	unicharset,
		FILE *	file,
		LIST *	training_samples
	)

This routine reads training samples from a file and places them into a data structure which organizes the samples by FontName and CharName. It then returns this data structure.

Parameters

file	open text file to read samples from
feature_defs
feature_name
max_samples
unicharset
training_samples

Returns

none

Note

Globals: none

Exceptions: none

History:

• Fri Aug 18 13:11:39 1989, DSJ, Created.
• Tue May 17 1998 simplifications to structure, illiminated font, and feature specification levels of structure.

Definition at line 394 of file commontraining.cpp.

                                                             {
  char    buffer[2048];
  char    unichar[UNICHAR_LEN + 1];
  LABELEDLIST char_sample;
  FEATURE_SET feature_samples;
  CHAR_DESC char_desc;
  int   i;
  int feature_type = ShortNameToFeatureType(feature_defs, feature_name);
  // Zero out the font_sample_count for all the classes.
  LIST it = *training_samples;
  iterate(it) {
    char_sample = reinterpret_cast<LABELEDLIST>(first_node(it));
    char_sample->font_sample_count = 0;
  }

  while (fgets(buffer, 2048, file) != NULL) {
    if (buffer[0] == '\n')
      continue;

    sscanf(buffer, "%*s %s", unichar);
    if (unicharset != NULL && !unicharset->contains_unichar(unichar)) {
      unicharset->unichar_insert(unichar);
      if (unicharset->size() > MAX_NUM_CLASSES) {
        tprintf("Error: Size of unicharset in training is "
                "greater than MAX_NUM_CLASSES\n");
        exit(1);
      }
    }
    char_sample = FindList(*training_samples, unichar);
    if (char_sample == NULL) {
      char_sample = NewLabeledList(unichar);
      *training_samples = push(*training_samples, char_sample);
    }
    char_desc = ReadCharDescription(feature_defs, file);
    feature_samples = char_desc->FeatureSets[feature_type];
    if (char_sample->font_sample_count < max_samples || max_samples <= 0) {
      char_sample->List = push(char_sample->List, feature_samples);
      char_sample->SampleCount++;
      char_sample->font_sample_count++;
    } else {
      FreeFeatureSet(feature_samples);
    }
    for (i = 0; i < char_desc->NumFeatureSets; i++) {
      if (feature_type != i)
        FreeFeatureSet(char_desc->FeatureSets[i]);
    }
    free(char_desc);
  }
}  // ReadTrainingSamples

LIST RemoveInsignificantProtos	(	LIST	ProtoList,
		BOOL8	KeepSigProtos,
		BOOL8	KeepInsigProtos,
		int	N
	)

Definition at line 633 of file commontraining.cpp.

{
  LIST NewProtoList = NIL_LIST;
  LIST pProtoList;
  PROTOTYPE* Proto;
  PROTOTYPE* NewProto;
  int i;

  pProtoList = ProtoList;
  iterate(pProtoList)
  {
    Proto = (PROTOTYPE *) first_node (pProtoList);
    if ((Proto->Significant && KeepSigProtos) ||
        (!Proto->Significant && KeepInsigProtos))
    {
      NewProto = (PROTOTYPE *)Emalloc(sizeof(PROTOTYPE));

      NewProto->Mean = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
      NewProto->Significant = Proto->Significant;
      NewProto->Style = Proto->Style;
      NewProto->NumSamples = Proto->NumSamples;
      NewProto->Cluster = NULL;
      NewProto->Distrib = NULL;

      for (i=0; i < N; i++)
        NewProto->Mean[i] = Proto->Mean[i];
      if (Proto->Variance.Elliptical != NULL)
      {
        NewProto->Variance.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
        for (i=0; i < N; i++)
          NewProto->Variance.Elliptical[i] = Proto->Variance.Elliptical[i];
      }
      else
        NewProto->Variance.Elliptical = NULL;
      //---------------------------------------------
      if (Proto->Magnitude.Elliptical != NULL)
      {
        NewProto->Magnitude.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
        for (i=0; i < N; i++)
          NewProto->Magnitude.Elliptical[i] = Proto->Magnitude.Elliptical[i];
      }
      else
        NewProto->Magnitude.Elliptical = NULL;
      //------------------------------------------------
      if (Proto->Weight.Elliptical != NULL)
      {
        NewProto->Weight.Elliptical = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
        for (i=0; i < N; i++)
          NewProto->Weight.Elliptical[i] = Proto->Weight.Elliptical[i];
      }
      else
        NewProto->Weight.Elliptical = NULL;

      NewProto->TotalMagnitude = Proto->TotalMagnitude;
      NewProto->LogMagnitude = Proto->LogMagnitude;
      NewProtoList = push_last(NewProtoList, NewProto);
    }
  }
  FreeProtoList(&ProtoList);
  return (NewProtoList);
}       /* RemoveInsignificantProtos */

CLUSTERER* SetUpForClustering	(	const FEATURE_DEFS_STRUCT &	FeatureDefs,
		LABELEDLIST	char_sample,
		const char *	program_feature_type
	)

This routine reads samples from a LABELEDLIST and enters those samples into a clusterer data structure. This data structure is then returned to the caller.

Parameters

char_sample	LABELEDLIST that holds all the feature information for a
FeatureDefs
program_feature_type	given character.

Returns

Pointer to new clusterer data structure.

Note

Globals: None

Exceptions: None

History: 8/16/89, DSJ, Created.

Definition at line 507 of file commontraining.cpp.

                                                                {
  uinT16 N;
  int i, j;
  FLOAT32 *Sample = NULL;
  CLUSTERER *Clusterer;
  inT32 CharID;
  LIST FeatureList = NULL;
  FEATURE_SET FeatureSet = NULL;

  int desc_index = ShortNameToFeatureType(FeatureDefs, program_feature_type);
  N = FeatureDefs.FeatureDesc[desc_index]->NumParams;
  Clusterer = MakeClusterer(N, FeatureDefs.FeatureDesc[desc_index]->ParamDesc);

  FeatureList = char_sample->List;
  CharID = 0;
  iterate(FeatureList) {
    FeatureSet = (FEATURE_SET) first_node(FeatureList);
    for (i = 0; i < FeatureSet->MaxNumFeatures; i++) {
      if (Sample == NULL)
        Sample = (FLOAT32 *)Emalloc(N * sizeof(FLOAT32));
      for (j = 0; j < N; j++)
        Sample[j] = FeatureSet->Features[i]->Params[j];
      MakeSample (Clusterer, Sample, CharID);
    }
    CharID++;
  }
  if ( Sample != NULL ) free( Sample );
  return( Clusterer );

}       /* SetUpForClustering */

CLASS_STRUCT* SetUpForFloat2Int	(	const UNICHARSET &	unicharset,
		LIST	LabeledClassList
	)

Definition at line 758 of file commontraining.cpp.

                                                       {
  MERGE_CLASS   MergeClass;
  CLASS_TYPE            Class;
  int                           NumProtos;
  int                           NumConfigs;
  int                           NumWords;
  int                           i, j;
  float                 Values[3];
  PROTO                 NewProto;
  PROTO                 OldProto;
  BIT_VECTOR            NewConfig;
  BIT_VECTOR            OldConfig;

  //    printf("Float2Int ...\n");

  CLASS_STRUCT* float_classes = new CLASS_STRUCT[unicharset.size()];
  iterate(LabeledClassList)
  {
    UnicityTableEqEq<int>   font_set;
    MergeClass = (MERGE_CLASS) first_node (LabeledClassList);
    Class = &float_classes[unicharset.unichar_to_id(MergeClass->Label)];
    NumProtos = MergeClass->Class->NumProtos;
    NumConfigs = MergeClass->Class->NumConfigs;
    font_set.move(&MergeClass->Class->font_set);
    Class->NumProtos = NumProtos;
    Class->MaxNumProtos = NumProtos;
    Class->Prototypes = (PROTO) Emalloc (sizeof(PROTO_STRUCT) * NumProtos);
    for(i=0; i < NumProtos; i++)
    {
      NewProto = ProtoIn(Class, i);
      OldProto = ProtoIn(MergeClass->Class, i);
      Values[0] = OldProto->X;
      Values[1] = OldProto->Y;
      Values[2] = OldProto->Angle;
      Normalize(Values);
      NewProto->X = OldProto->X;
      NewProto->Y = OldProto->Y;
      NewProto->Length = OldProto->Length;
      NewProto->Angle = OldProto->Angle;
      NewProto->A = Values[0];
      NewProto->B = Values[1];
      NewProto->C = Values[2];
    }

    Class->NumConfigs = NumConfigs;
    Class->MaxNumConfigs = NumConfigs;
    Class->font_set.move(&font_set);
    Class->Configurations = (BIT_VECTOR*) Emalloc (sizeof(BIT_VECTOR) * NumConfigs);
    NumWords = WordsInVectorOfSize(NumProtos);
    for(i=0; i < NumConfigs; i++)
    {
      NewConfig = NewBitVector(NumProtos);
      OldConfig = MergeClass->Class->Configurations[i];
      for(j=0; j < NumWords; j++)
        NewConfig[j] = OldConfig[j];
      Class->Configurations[i] = NewConfig;
    }
  }
  return float_classes;
} // SetUpForFloat2Int

STRING_PARAM_FLAG	(	configfile	,
		""	,
		"File to load more configs from"
	)

STRING_PARAM_FLAG	(	D	,
		""	,
		"Directory to write output files to"
	)

STRING_PARAM_FLAG	(	F	,
		"font_properties"	,
		"File listing font properties"
	)

STRING_PARAM_FLAG	(	X	,
		""	,
		"File listing font xheights"
	)

STRING_PARAM_FLAG	(	U	,
		"unicharset"	,
		"File to load unicharset from"
	)

STRING_PARAM_FLAG	(	O	,
		""	,
		"File to write unicharset to"
	)

STRING_PARAM_FLAG	(	T	,
		""	,
		"File to load trainer from"
	)

STRING_PARAM_FLAG	(	output_trainer	,
		""	,
		"File to write trainer to"
	)

STRING_PARAM_FLAG	(	test_ch	,
		""	,
		"UTF8 test character string"
	)

Variable Documentation

CCUtil ccutil

Definition at line 53 of file commontraining.cpp.

CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 }

Definition at line 51 of file commontraining.cpp.

FEATURE_DEFS_STRUCT feature_defs

Definition at line 52 of file commontraining.cpp.