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tesseract/training/commontraining.cpp

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// Copyright 2008 Google Inc. All Rights Reserved.
// Author: scharron@google.com (Samuel Charron)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "commontraining.h"
#include "oldlist.h"
#include "globals.h"
#include "mf.h"
#include "clusttool.h"
#include "cluster.h"
#include "mergenf.h"
#include "tessopt.h"
#include "featdefs.h"
#include "efio.h"
#include "emalloc.h"
#include "tprintf.h"
#include "freelist.h"
#include "unicity_table.h"
#include <math.h>
#define round(x,frag)(floor(x/frag+.5)*frag)
// Global Variables.
char *Directory = NULL;
const char *InputUnicharsetFile = NULL;
const char *OutputUnicharsetFile = NULL;
const char *InputFontInfoFile = NULL;
const char *InputXHeightsFile = NULL;
FLOAT32 RoundingAccuracy = 0.0f;
char CTFontName[MAXNAMESIZE];
const char* test_ch = "";
/*---------------------------------------------------------------------------*/
void ParseArguments(int argc, char **argv) {
/*
** Parameters:
** argc number of command line arguments to parse
** argv command line arguments
** Globals:
** ShowSignificantProtos flag controlling proto display
** ShowInsignificantProtos flag controlling proto display
** Config current clustering parameters
** tessoptarg, tessoptind defined by tessopt sys call
** Argc, Argv global copies of argc and argv
** Operation:
** This routine parses the command line arguments that were
** passed to the program. The legal arguments are:
** -d "turn off display of samples"
** -S [ spherical | elliptical | mixed | automatic ]
** -M MinSamples "min samples per prototype (%)"
** -B MaxIllegal "max illegal chars per cluster (%)"
** -I Independence "0 to 1"
** -C Confidence "1e-200 to 1.0"
** -D Directory
** -R RoundingAccuracy
** -U InputUnicharsetFile
** -O OutputUnicharsetFile
** -X InputXHeightsFile
** Return: none
** Exceptions: Illegal options terminate the program.
** History: 7/24/89, DSJ, Created.
*/
int Option;
int ParametersRead;
BOOL8 Error;
Error = FALSE;
while ((Option = tessopt(argc, argv, "F:O:U:R:D:C:I:M:B:S:X:")) != EOF) {
switch (Option) {
case 'C':
ParametersRead = sscanf( tessoptarg, "%lf", &(Config.Confidence) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.Confidence > 1 ) Config.Confidence = 1;
else if ( Config.Confidence < 0 ) Config.Confidence = 0;
break;
case 'I':
ParametersRead = sscanf( tessoptarg, "%f", &(Config.Independence) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.Independence > 1 ) Config.Independence = 1;
else if ( Config.Independence < 0 ) Config.Independence = 0;
break;
case 'M':
ParametersRead = sscanf( tessoptarg, "%f", &(Config.MinSamples) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.MinSamples > 1 ) Config.MinSamples = 1;
else if ( Config.MinSamples < 0 ) Config.MinSamples = 0;
break;
case 'B':
ParametersRead = sscanf( tessoptarg, "%f", &(Config.MaxIllegal) );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( Config.MaxIllegal > 1 ) Config.MaxIllegal = 1;
else if ( Config.MaxIllegal < 0 ) Config.MaxIllegal = 0;
break;
case 'R':
ParametersRead = sscanf( tessoptarg, "%f", &RoundingAccuracy );
if ( ParametersRead != 1 ) Error = TRUE;
else if ( RoundingAccuracy > 0.01f ) RoundingAccuracy = 0.01f;
else if ( RoundingAccuracy < 0.0f ) RoundingAccuracy = 0.0f;
break;
case 'S':
switch ( tessoptarg[0] )
{
case 's': Config.ProtoStyle = spherical; break;
case 'e': Config.ProtoStyle = elliptical; break;
case 'm': Config.ProtoStyle = mixed; break;
case 'a': Config.ProtoStyle = automatic; break;
default: Error = TRUE;
}
break;
case 'D':
Directory = tessoptarg;
break;
case 'U':
InputUnicharsetFile = tessoptarg;
break;
case 'O':
OutputUnicharsetFile = tessoptarg;
break;
case 'F':
InputFontInfoFile = tessoptarg;
break;
case 'X':
InputXHeightsFile = tessoptarg;
printf("InputXHeightsFile %s\n", InputXHeightsFile);
break;
case '?':
Error = TRUE;
break;
}
if ( Error )
{
fprintf (stderr, "usage: %s [-d] [-p] [-n]\n", argv[0] );
fprintf (stderr, "\t[-S ProtoStyle]\n");
fprintf (stderr, "\t[-M MinSamples] [-B MaxBad] [-I Independence]\n");
fprintf (stderr, "\t[-C Confidence] [-D Directory]\n");
fprintf (stderr, "\t[-U InputUnicharsetFile] [-O OutputUnicharsetFile]\n");
fprintf (stderr, "\t[-F FontInfoFile]\n");
fprintf (stderr, "\t[-X InputXHeightsFile]\n");
fprintf (stderr, "\t[ TrainingPage ... ]\n");
exit (2);
}
}
} // ParseArguments
/*---------------------------------------------------------------------------*/
char *GetNextFilename (int Argc, char** Argv)
/*
** Parameters: none
** Globals:
** tessoptind defined by tessopt sys call
** Operation:
** 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.
** Return: Next command line argument or NULL.
** Exceptions: none
** History: Fri Aug 18 09:34:12 1989, DSJ, Created.
*/
{
if (tessoptind < Argc)
return (Argv [tessoptind++]);
else
return (NULL);
} /* GetNextFilename */
/*---------------------------------------------------------------------------*/
LABELEDLIST FindList (
LIST List,
char *Label)
/*
** Parameters:
** List list to search
** Label label to search for
** Globals: none
** Operation:
** 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.
** Return: Labeled list with the specified Label or NULL.
** Exceptions: none
** History: Fri Aug 18 15:57:41 1989, DSJ, Created.
*/
{
LABELEDLIST LabeledList;
iterate (List)
{
LabeledList = (LABELEDLIST) first_node (List);
if (strcmp (LabeledList->Label, Label) == 0)
return (LabeledList);
}
return (NULL);
} /* FindList */
/*---------------------------------------------------------------------------*/
LABELEDLIST NewLabeledList (
const char *Label)
/*
** Parameters:
** Label label for new list
** Globals: none
** Operation:
** This routine allocates a new, empty labeled list and gives
** it the specified label.
** Return: New, empty labeled list.
** Exceptions: none
** History: Fri Aug 18 16:08:46 1989, DSJ, Created.
*/
{
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 ReadTrainingSamples(const FEATURE_DEFS_STRUCT& feature_defs,
const char *feature_name, int max_samples,
float linear_spread, float circular_spread,
UNICHARSET* unicharset,
FILE* file, LIST* training_samples) {
/*
** Parameters:
** file open text file to read samples from
** Globals: none
** Operation:
** 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.
** Return: 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.
*/
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);
// Description of feature of type feature_type.
const FEATURE_DESC_STRUCT* f_desc = feature_defs.FeatureDesc[feature_type];
// 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 (fscanf(file, "%s %s", CTFontName, unichar) == 2) {
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) {
for (int feature = 0; feature < feature_samples->NumFeatures; ++feature) {
FEATURE f = feature_samples->Features[feature];
for (int dim =0; dim < f->Type->NumParams; ++dim)
f->Params[dim] += f_desc->ParamDesc[dim].Circular
? UniformRandomNumber(-circular_spread, circular_spread)
: UniformRandomNumber(-linear_spread, linear_spread);
}
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
/*---------------------------------------------------------------------------*/
void WriteTrainingSamples (
const FEATURE_DEFS_STRUCT &FeatureDefs,
char *Directory,
LIST CharList,
const char* program_feature_type)
/*
** Parameters:
** Directory directory to place sample files into
** FontList list of fonts used in the training samples
** Operation:
** This routine writes the specified samples into files which
** are organized according to the font name and character name
** of the samples.
** Return: none
** Exceptions: none
** History: Fri Aug 18 16:17:06 1989, DSJ, Created.
*/
{
LABELEDLIST char_sample;
FEATURE_SET FeatureSet;
LIST FeatureList;
FILE *File;
char Filename[MAXNAMESIZE];
int NumSamples;
iterate (CharList) // iterate thru all of the fonts
{
char_sample = (LABELEDLIST) first_node (CharList);
// construct the full pathname for the current samples file
strcpy (Filename, "");
if (Directory != NULL)
{
strcat (Filename, Directory);
strcat (Filename, "/");
}
strcat (Filename, CTFontName);
strcat (Filename, "/");
strcat (Filename, char_sample->Label);
strcat (Filename, ".");
strcat (Filename, program_feature_type);
printf ("\nWriting %s ...", Filename);
/* if file does not exist, create a new one with an appropriate
header; otherwise append samples to the existing file */
File = fopen (Filename, "r");
if (File == NULL)
{
File = Efopen (Filename, "w");
WriteOldParamDesc(
File,
FeatureDefs.FeatureDesc[ShortNameToFeatureType(
FeatureDefs, program_feature_type)]);
}
else
{
fclose (File);
File = Efopen (Filename, "a");
}
// append samples onto the file
FeatureList = char_sample->List;
NumSamples = 0;
iterate (FeatureList)
{
FeatureSet = (FEATURE_SET) first_node (FeatureList);
WriteFeatureSet (File, FeatureSet);
NumSamples++;
}
fclose (File);
}
} /* WriteTrainingSamples */
/*---------------------------------------------------------------------------*/
void FreeTrainingSamples (
LIST CharList)
/*
** Parameters:
** FontList list of all fonts in document
** Globals: none
** Operation:
** This routine deallocates all of the space allocated to
** the specified list of training samples.
** Return: none
** Exceptions: none
** History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/
{
LABELEDLIST char_sample;
FEATURE_SET FeatureSet;
LIST FeatureList;
// printf ("FreeTrainingSamples...\n");
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 */
/*---------------------------------------------------------------------------*/
void FreeLabeledList (
LABELEDLIST LabeledList)
/*
** Parameters:
** LabeledList labeled list to be freed
** Globals: none
** Operation:
** 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.
** Return: none
** Exceptions: none
** History: Fri Aug 18 17:52:45 1989, DSJ, Created.
*/
{
destroy (LabeledList->List);
free (LabeledList->Label);
free (LabeledList);
} /* FreeLabeledList */
/*---------------------------------------------------------------------------*/
CLUSTERER *SetUpForClustering(
const FEATURE_DEFS_STRUCT &FeatureDefs,
LABELEDLIST char_sample,
const char* program_feature_type)
/*
** Parameters:
** char_sample: LABELEDLIST that holds all the feature information for a
** given character.
** Globals:
** None
** Operation:
** 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.
** Return:
** Pointer to new clusterer data structure.
** Exceptions:
** None
** History:
** 8/16/89, DSJ, Created.
*/
{
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++)
if (RoundingAccuracy != 0.0f)
Sample[j] = round(FeatureSet->Features[i]->Params[j], RoundingAccuracy);
else
Sample[j] = FeatureSet->Features[i]->Params[j];
MakeSample (Clusterer, Sample, CharID);
}
CharID++;
}
if ( Sample != NULL ) free( Sample );
return( Clusterer );
} /* SetUpForClustering */
/*------------------------------------------------------------------------*/
void MergeInsignificantProtos(LIST ProtoList, const char* label,
CLUSTERER *Clusterer, CLUSTERCONFIG *Config) {
PROTOTYPE *Prototype;
bool debug = strcmp(test_ch, 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 */
/*-----------------------------------------------------------------------------*/
void CleanUpUnusedData(
LIST ProtoList)
{
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;
}
}
}
/*------------------------------------------------------------------------*/
LIST RemoveInsignificantProtos(
LIST ProtoList,
BOOL8 KeepSigProtos,
BOOL8 KeepInsigProtos,
int N)
{
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 */
/*----------------------------------------------------------------------------*/
MERGE_CLASS FindClass (
LIST List,
char *Label)
{
MERGE_CLASS MergeClass;
iterate (List)
{
MergeClass = (MERGE_CLASS) first_node (List);
if (strcmp (MergeClass->Label, Label) == 0)
return (MergeClass);
}
return (NULL);
} /* FindClass */
/*---------------------------------------------------------------------------*/
MERGE_CLASS NewLabeledClass (
char *Label)
{
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 */
/*-----------------------------------------------------------------------------*/
void FreeLabeledClassList (
LIST ClassList)
/*
** Parameters:
** FontList list of all fonts in document
** Globals: none
** Operation:
** This routine deallocates all of the space allocated to
** the specified list of training samples.
** Return: none
** Exceptions: none
** History: Fri Aug 18 17:44:27 1989, DSJ, Created.
*/
{
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 */
/** SetUpForFloat2Int **************************************************/
void SetUpForFloat2Int(const UNICHARSET& unicharset, LIST LabeledClassList) {
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");
iterate(LabeledClassList)
{
UnicityTableEqEq<int> font_set;
MergeClass = (MERGE_CLASS) first_node (LabeledClassList);
Class = &TrainingData[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;
}
}
} // SetUpForFloat2Int
/*--------------------------------------------------------------------------*/
void Normalize (
float *Values)
{
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
/*-------------------------------------------------------------------------*/
void FreeNormProtoList (
LIST CharList)
{
LABELEDLIST char_sample;
iterate (CharList) /* iterate thru all of the fonts */
{
char_sample = (LABELEDLIST) first_node (CharList);
FreeLabeledList (char_sample);
}
destroy (CharList);
} // FreeNormProtoList
/*---------------------------------------------------------------------------*/
void AddToNormProtosList(
LIST* NormProtoList,
LIST ProtoList,
char* CharName)
{
PROTOTYPE* Proto;
LABELEDLIST LabeledProtoList;
LabeledProtoList = NewLabeledList(CharName);
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node (ProtoList);
LabeledProtoList->List = push(LabeledProtoList->List, Proto);
}
*NormProtoList = push(*NormProtoList, LabeledProtoList);
}
/*---------------------------------------------------------------------------*/
int NumberOfProtos(
LIST ProtoList,
BOOL8 CountSigProtos,
BOOL8 CountInsigProtos)
{
int N = 0;
PROTOTYPE *Proto;
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node ( ProtoList );
if (( Proto->Significant && CountSigProtos ) ||
( ! Proto->Significant && CountInsigProtos ) )
N++;
}
return(N);
}
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