/*---------------------------------------------------------------------------*/
int main (int argc, char **argv) {
/*
** Parameters:
** argc number of command line arguments
** argv array of command line arguments
** Globals: none
** Operation:
** This program reads in a text file consisting of feature
** samples from a training page in the following format:
**
** FontName CharName NumberOfFeatureTypes(N)
** FeatureTypeName1 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FeatureTypeName2 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** ...
** FeatureTypeNameN NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FontName CharName ...
**
** The result of this program is a binary inttemp file used by
** the OCR engine.
** Return: none
** Exceptions: none
** History: Fri Aug 18 08:56:17 1989, DSJ, Created.
** Mon May 18 1998, Christy Russson, Revistion started.
*/
char *PageName;
FILE *TrainingPage;
FILE *OutFile;
LIST CharList;
CLUSTERER *Clusterer = NULL;
LIST ProtoList = NIL;
LABELEDLIST CharSample;
PROTOTYPE *Prototype;
LIST ClassList = NIL;
int Cid, Pid;
PROTO Proto;
PROTO_STRUCT DummyProto;
BIT_VECTOR Config2;
MERGE_CLASS MergeClass;
INT_TEMPLATES IntTemplates;
LIST pCharList, pProtoList;
char Filename[MAXNAMESIZE];
tesseract::Classify classify;
ParseArguments (argc, argv);
if (InputUnicharsetFile == NULL) {
InputUnicharsetFile = kInputUnicharsetFile;
}
if (OutputUnicharsetFile == NULL) {
OutputUnicharsetFile = kOutputUnicharsetFile;
}
if (!unicharset_training.load_from_file(InputUnicharsetFile)) {
fprintf(stderr, "Failed to load unicharset from file %s\n"
"Building unicharset for mftraining from scratch...\n",
InputUnicharsetFile);
unicharset_training.clear();
// Space character needed to represent NIL classification.
unicharset_training.unichar_insert(" ");
}
if (InputFontInfoFile != NULL) {
FILE* f = fopen(InputFontInfoFile, "r");
if (f == NULL) {
fprintf(stderr, "Failed to load font_properties\n");
} else {
int italic, bold, fixed, serif, fraktur;
while (!feof(f)) {
FontInfo fontinfo;
fontinfo.name = new char[1024];
fontinfo.properties = 0;
if (fscanf(f, "%1024s %i %i %i %i %i\n", fontinfo.name,
&italic, &bold, &fixed, &serif, &fraktur) != 6)
continue;
fontinfo.properties =
(italic << 0) +
(bold << 1) +
(fixed << 2) +
(serif << 3) +
(fraktur << 4);
if (!classify.get_fontinfo_table().contains(fontinfo)) {
classify.get_fontinfo_table().push_back(fontinfo);
} else {
fprintf(stderr, "Font %s already defined\n", fontinfo.name);
return 1;
}
}
fclose(f);
}
}
while ((PageName = GetNextFilename(argc, argv)) != NULL) {
printf ("Reading %s ...\n", PageName);
char *short_name = strrchr(PageName, '/');
if (short_name == NULL)
short_name = PageName;
else
++short_name;
// filename is expected to be of the form [lang].[fontname].exp[num].tr
// If it is, then set short_name to be the [fontname]. Otherwise it is just
// the file basename with the .tr extension removed.
char *font_dot = strchr(short_name, '.');
char *exp_dot = (font_dot != NULL) ? strstr(font_dot, ".exp") : NULL;
if (font_dot != NULL && exp_dot != NULL && font_dot != exp_dot) {
short_name = new_dup(font_dot + 1);
short_name[exp_dot - font_dot - 1] = '\0';
} else {
short_name = new_dup(short_name);
int len = strlen(short_name);
if (!strcmp(short_name + len - 3, ".tr"))
short_name[len - 3] = '\0';
}
int fontinfo_id;
FontInfo fontinfo;
fontinfo.name = short_name;
fontinfo.properties = 0; // Not used to lookup in the table
if (!classify.get_fontinfo_table().contains(fontinfo)) {
fontinfo_id = classify.get_fontinfo_table().push_back(fontinfo);
printf("%s has no defined properties.\n", short_name);
} else {
fontinfo_id = classify.get_fontinfo_table().get_id(fontinfo);
// Update the properties field
fontinfo = classify.get_fontinfo_table().get(fontinfo_id);
delete[] short_name;
}
TrainingPage = Efopen (PageName, "r");
CharList = ReadTrainingSamples (TrainingPage);
fclose (TrainingPage);
//WriteTrainingSamples (Directory, CharList);
pCharList = CharList;
iterate(pCharList) {
//Cluster
CharSample = (LABELEDLIST) first_node (pCharList);
// printf ("\nClustering %s ...", CharSample->Label);
Clusterer = SetUpForClustering(CharSample, PROGRAM_FEATURE_TYPE);
Config.MagicSamples = CharSample->SampleCount;
ProtoList = ClusterSamples(Clusterer, &Config);
CleanUpUnusedData(ProtoList);
//Merge
MergeInsignificantProtos(ProtoList, CharSample->Label,
Clusterer, &Config);
if (strcmp(test_ch, CharSample->Label) == 0)
DisplayProtoList(test_ch, ProtoList);
ProtoList = RemoveInsignificantProtos(ProtoList, ShowSignificantProtos,
ShowInsignificantProtos,
Clusterer->SampleSize);
FreeClusterer(Clusterer);
MergeClass = FindClass (ClassList, CharSample->Label);
if (MergeClass == NULL) {
MergeClass = NewLabeledClass (CharSample->Label);
ClassList = push (ClassList, MergeClass);
}
Cid = AddConfigToClass(MergeClass->Class);
MergeClass->Class->font_set.push_back(fontinfo_id);
pProtoList = ProtoList;
iterate (pProtoList) {
Prototype = (PROTOTYPE *) first_node (pProtoList);
// see if proto can be approximated by existing proto
Pid = FindClosestExistingProto(MergeClass->Class,
MergeClass->NumMerged, Prototype);
if (Pid == NO_PROTO) {
Pid = AddProtoToClass (MergeClass->Class);
Proto = ProtoIn (MergeClass->Class, Pid);
MakeNewFromOld (Proto, Prototype);
MergeClass->NumMerged[Pid] = 1;
}
else {
MakeNewFromOld (&DummyProto, Prototype);
ComputeMergedProto (ProtoIn (MergeClass->Class, Pid), &DummyProto,
(FLOAT32) MergeClass->NumMerged[Pid], 1.0,
ProtoIn (MergeClass->Class, Pid));
MergeClass->NumMerged[Pid] ++;
}
Config2 = MergeClass->Class->Configurations[Cid];
AddProtoToConfig (Pid, Config2);
}
FreeProtoList (&ProtoList);
}
FreeTrainingSamples (CharList);
}
//WriteMergedTrainingSamples(Directory,ClassList);
WriteMicrofeat(Directory, ClassList);
SetUpForFloat2Int(ClassList);
IntTemplates = classify.CreateIntTemplates(TrainingData,
unicharset_training);
strcpy (Filename, "");
if (Directory != NULL) {
strcat (Filename, Directory);
strcat (Filename, "/");
}
strcat (Filename, "inttemp");
#ifdef __UNIX__
OutFile = Efopen (Filename, "w");
#else
OutFile = Efopen (Filename, "wb");
#endif
classify.WriteIntTemplates(OutFile, IntTemplates, unicharset_training);
fclose (OutFile);
strcpy (Filename, "");
if (Directory != NULL) {
strcat (Filename, Directory);
strcat (Filename, "/");
}
strcat (Filename, "pffmtable");
// Now create pffmtable.
WritePFFMTable(IntTemplates, Filename);
// Write updated unicharset to a file.
if (!unicharset_training.save_to_file(OutputUnicharsetFile)) {
fprintf(stderr, "Failed to save unicharset to file %s\n",
OutputUnicharsetFile);
exit(1);
}
printf ("Done!\n"); /**/
FreeLabeledClassList (ClassList);
return 0;
} /* main */
/*---------------------------------------------------------------------------*/
int main (
int argc,
char **argv)
/*
** Parameters:
** argc number of command line arguments
** argv array of command line arguments
** Globals: none
** Operation:
** This program reads in a text file consisting of feature
** samples from a training page in the following format:
**
** FontName CharName NumberOfFeatureTypes(N)
** FeatureTypeName1 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FeatureTypeName2 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** ...
** FeatureTypeNameN NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FontName CharName ...
**
** It then appends these samples into a separate file for each
** character. The name of the file is
**
** DirectoryName/FontName/CharName.FeatureTypeName
**
** The DirectoryName can be specified via a command
** line argument. If not specified, it defaults to the
** current directory. The format of the resulting files is:
**
** NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** NumberOfFeatures(M)
** ...
**
** The output files each have a header which describes the
** type of feature which the file contains. This header is
** in the format required by the clusterer. A command line
** argument can also be used to specify that only the first
** N samples of each class should be used.
** Return: none
** Exceptions: none
** History: Fri Aug 18 08:56:17 1989, DSJ, Created.
*/
{
char *PageName;
FILE *TrainingPage;
LIST CharList = NIL_LIST;
CLUSTERER *Clusterer = NULL;
LIST ProtoList = NIL_LIST;
LIST NormProtoList = NIL_LIST;
LIST pCharList;
LABELEDLIST CharSample;
FEATURE_DEFS_STRUCT FeatureDefs;
InitFeatureDefs(&FeatureDefs);
ParseArguments(argc, argv);
int num_fonts = 0;
while ((PageName = GetNextFilename(argc, argv)) != NULL) {
printf("Reading %s ...\n", PageName);
TrainingPage = Efopen(PageName, "r");
ReadTrainingSamples(FeatureDefs, PROGRAM_FEATURE_TYPE,
100, 1.0f / 64.0f, 0.0f, NULL, TrainingPage, &CharList);
fclose(TrainingPage);
++num_fonts;
}
printf("Clustering ...\n");
// To allow an individual font to form a separate cluster,
// reduce the min samples:
// Config.MinSamples = 0.5 / num_fonts;
pCharList = CharList;
iterate(pCharList) {
//Cluster
CharSample = (LABELEDLIST)first_node(pCharList);
Clusterer =
SetUpForClustering(FeatureDefs, CharSample, PROGRAM_FEATURE_TYPE);
float SavedMinSamples = Config.MinSamples;
// To disable the tendency to produce a single cluster for all fonts,
// make MagicSamples an impossible to achieve number:
// Config.MagicSamples = CharSample->SampleCount * 10;
Config.MagicSamples = CharSample->SampleCount;
while (Config.MinSamples > 0.001) {
ProtoList = ClusterSamples(Clusterer, &Config);
if (NumberOfProtos(ProtoList, 1, 0) > 0) {
break;
} else {
Config.MinSamples *= 0.95;
printf("0 significant protos for %s."
" Retrying clustering with MinSamples = %f%%\n",
CharSample->Label, Config.MinSamples);
}
}
Config.MinSamples = SavedMinSamples;
AddToNormProtosList(&NormProtoList, ProtoList, CharSample->Label);
}
FreeTrainingSamples(CharList);
if (Clusterer == NULL) // To avoid a SIGSEGV
return 1;
WriteNormProtos (Directory, NormProtoList, Clusterer);
FreeClusterer(Clusterer);
FreeProtoList(&ProtoList);
FreeNormProtoList(NormProtoList);
printf ("\n");
return 0;
} // main
/**
* Creates a MasterTraininer and loads the training data into it:
* Initializes feature_defs and IntegerFX.
* Loads the shape_table if shape_table != nullptr.
* Loads initial unicharset from -U command-line option.
* If FLAGS_T is set, loads the majority of data from there, else:
* - Loads font info from -F option.
* - Loads xheights from -X option.
* - Loads samples from .tr files in remaining command-line args.
* - Deletes outliers and computes canonical samples.
* - If FLAGS_output_trainer is set, saves the trainer for future use.
* Computes canonical and cloud features.
* If shape_table is not nullptr, but failed to load, make a fake flat one,
* as shape clustering was not run.
*/
MasterTrainer* LoadTrainingData(int argc, const char* const * argv,
bool replication,
ShapeTable** shape_table,
STRING* file_prefix) {
InitFeatureDefs(&feature_defs);
InitIntegerFX();
*file_prefix = "";
if (!FLAGS_D.empty()) {
*file_prefix += FLAGS_D.c_str();
*file_prefix += "/";
}
// If we are shape clustering (nullptr shape_table) or we successfully load
// a shape_table written by a previous shape clustering, then
// shape_analysis will be true, meaning that the MasterTrainer will replace
// some members of the unicharset with their fragments.
bool shape_analysis = false;
if (shape_table != nullptr) {
*shape_table = LoadShapeTable(*file_prefix);
if (*shape_table != nullptr) shape_analysis = true;
} else {
shape_analysis = true;
}
MasterTrainer* trainer = new MasterTrainer(NM_CHAR_ANISOTROPIC,
shape_analysis,
replication,
FLAGS_debug_level);
IntFeatureSpace fs;
fs.Init(kBoostXYBuckets, kBoostXYBuckets, kBoostDirBuckets);
if (FLAGS_T.empty()) {
trainer->LoadUnicharset(FLAGS_U.c_str());
// Get basic font information from font_properties.
if (!FLAGS_F.empty()) {
if (!trainer->LoadFontInfo(FLAGS_F.c_str())) {
delete trainer;
return nullptr;
}
}
if (!FLAGS_X.empty()) {
if (!trainer->LoadXHeights(FLAGS_X.c_str())) {
delete trainer;
return nullptr;
}
}
trainer->SetFeatureSpace(fs);
const char* page_name;
// Load training data from .tr files on the command line.
while ((page_name = GetNextFilename(argc, argv)) != nullptr) {
tprintf("Reading %s ...\n", page_name);
trainer->ReadTrainingSamples(page_name, feature_defs, false);
// If there is a file with [lang].[fontname].exp[num].fontinfo present,
// read font spacing information in to fontinfo_table.
int pagename_len = strlen(page_name);
char *fontinfo_file_name = new char[pagename_len + 7];
strncpy(fontinfo_file_name, page_name, pagename_len - 2); // remove "tr"
strcpy(fontinfo_file_name + pagename_len - 2, "fontinfo"); // +"fontinfo"
trainer->AddSpacingInfo(fontinfo_file_name);
delete[] fontinfo_file_name;
// Load the images into memory if required by the classifier.
if (FLAGS_load_images) {
STRING image_name = page_name;
// Chop off the tr and replace with tif. Extension must be tif!
image_name.truncate_at(image_name.length() - 2);
image_name += "tif";
trainer->LoadPageImages(image_name.string());
}
}
trainer->PostLoadCleanup();
// Write the master trainer if required.
if (!FLAGS_output_trainer.empty()) {
FILE* fp = fopen(FLAGS_output_trainer.c_str(), "wb");
if (fp == nullptr) {
tprintf("Can't create saved trainer data!\n");
} else {
trainer->Serialize(fp);
fclose(fp);
}
}
} else {
bool success = false;
tprintf("Loading master trainer from file:%s\n",
FLAGS_T.c_str());
FILE* fp = fopen(FLAGS_T.c_str(), "rb");
if (fp == nullptr) {
tprintf("Can't read file %s to initialize master trainer\n",
FLAGS_T.c_str());
} else {
success = trainer->DeSerialize(false, fp);
fclose(fp);
}
if (!success) {
tprintf("Deserialize of master trainer failed!\n");
delete trainer;
return nullptr;
}
trainer->SetFeatureSpace(fs);
}
trainer->PreTrainingSetup();
if (!FLAGS_O.empty() &&
!trainer->unicharset().save_to_file(FLAGS_O.c_str())) {
fprintf(stderr, "Failed to save unicharset to file %s\n", FLAGS_O.c_str());
delete trainer;
return nullptr;
}
if (shape_table != nullptr) {
// If we previously failed to load a shapetable, then shape clustering
// wasn't run so make a flat one now.
if (*shape_table == nullptr) {
*shape_table = new ShapeTable;
trainer->SetupFlatShapeTable(*shape_table);
tprintf("Flat shape table summary: %s\n",
(*shape_table)->SummaryStr().string());
}
(*shape_table)->set_unicharset(trainer->unicharset());
}
return trainer;
}
/**
* \brief Open the next file.
*
* Opens the file contaning next job data.\n
*/
int File_OpenDataStream(void)
{
/* PMS_SHOW("File_OpenDataStream()\n"); */
if(!GetNextFilename(szJobFilename))
return -1; /* -1 means don't try opening this module again */
if ( (l_fileCurrent = fopen((char *)szJobFilename, "rb") ) == NULL )
{
PMS_SHOW_ERROR(" ***ASSERT*** File_OpenDataStream: Failed to open file %s \n", szJobFilename);
return 0; /* 0 means you may try this module again to try the next job */
}
if(g_tSystemInfo.nStoreJobBeforeRip)
{
int nRead;
int nChunk = 16 * 1024;
PMS_SHOW("Storing job before passing to rip...\n");
l_pStoreBuffer = OSMalloc(g_tSystemInfo.cbReceiveBuffer,PMS_MemoryPoolMisc);
if(!l_pStoreBuffer) {
PMS_SHOW_ERROR(" ***ASSERT*** File_OpenDataStream: Failed to allocate buffer for storing job\n");
fclose(l_fileCurrent);
l_fileCurrent = NULL;
return 0; /* 0 means you may try this module again to try the next job */
}
fseek(l_fileCurrent,0,SEEK_END);
l_nStoreBufferUsed = ftell(l_fileCurrent);
fseek(l_fileCurrent,0,SEEK_SET);
if(l_nStoreBufferUsed > g_tSystemInfo.cbReceiveBuffer)
{
PMS_SHOW_ERROR(" ***ASSERT*** File_OpenDataStream: Failed to store job. Job size is larger than the memory buffer.\n");
fclose(l_fileCurrent);
l_fileCurrent = NULL;
OSFree(l_pStoreBuffer, PMS_MemoryPoolMisc);
l_pStoreBuffer = NULL;
return 0; /* 0 means you may try this module again to try the next job */
}
l_nStoreBufferUsed = 0;
l_nStoreBufferPos = 0;
do
{
if((l_nStoreBufferUsed + nChunk) < g_tSystemInfo.cbReceiveBuffer)
{
nRead = (int)fread(l_pStoreBuffer + l_nStoreBufferUsed, 1, nChunk, l_fileCurrent);
}
else if(l_nStoreBufferUsed < g_tSystemInfo.cbReceiveBuffer)
{
nRead = (int)fread(l_pStoreBuffer + l_nStoreBufferUsed, 1, g_tSystemInfo.cbReceiveBuffer - l_nStoreBufferUsed, l_fileCurrent);
}
else
{
nRead = 0;
}
l_nStoreBufferUsed += nRead;
} while (nRead>0);
fclose(l_fileCurrent);
l_fileCurrent = NULL;
PMS_SHOW("Stored %d bytes.\n", l_nStoreBufferUsed);
}
return 1; /* 1 means we have a job ready to rip */
}
/**
* This program reads in a text file consisting of feature
* samples from a training page in the following format:
* @verbatim
FontName CharName NumberOfFeatureTypes(N)
FeatureTypeName1 NumberOfFeatures(M)
Feature1
...
FeatureM
FeatureTypeName2 NumberOfFeatures(M)
Feature1
...
FeatureM
...
FeatureTypeNameN NumberOfFeatures(M)
Feature1
...
FeatureM
FontName CharName ...
@endverbatim
* It then appends these samples into a separate file for each
* character. The name of the file is
*
* DirectoryName/FontName/CharName.FeatureTypeName
*
* The DirectoryName can be specified via a command
* line argument. If not specified, it defaults to the
* current directory. The format of the resulting files is:
* @verbatim
NumberOfFeatures(M)
Feature1
...
FeatureM
NumberOfFeatures(M)
...
@endverbatim
* The output files each have a header which describes the
* type of feature which the file contains. This header is
* in the format required by the clusterer. A command line
* argument can also be used to specify that only the first
* N samples of each class should be used.
* @param argc number of command line arguments
* @param argv array of command line arguments
* @return none
* @note Globals: none
* @note Exceptions: none
* @note History: Fri Aug 18 08:56:17 1989, DSJ, Created.
*/
int main(int argc, char *argv[]) {
// Set the global Config parameters before parsing the command line.
Config = CNConfig;
const char *PageName;
FILE *TrainingPage;
LIST CharList = NIL_LIST;
CLUSTERER *Clusterer = NULL;
LIST ProtoList = NIL_LIST;
LIST NormProtoList = NIL_LIST;
LIST pCharList;
LABELEDLIST CharSample;
FEATURE_DEFS_STRUCT FeatureDefs;
InitFeatureDefs(&FeatureDefs);
ParseArguments(&argc, &argv);
int num_fonts = 0;
while ((PageName = GetNextFilename(argc, argv)) != NULL) {
printf("Reading %s ...\n", PageName);
TrainingPage = Efopen(PageName, "rb");
ReadTrainingSamples(FeatureDefs, PROGRAM_FEATURE_TYPE,
100, NULL, TrainingPage, &CharList);
fclose(TrainingPage);
++num_fonts;
}
printf("Clustering ...\n");
// To allow an individual font to form a separate cluster,
// reduce the min samples:
// Config.MinSamples = 0.5 / num_fonts;
pCharList = CharList;
// The norm protos will count the source protos, so we keep them here in
// freeable_protos, so they can be freed later.
GenericVector<LIST> freeable_protos;
iterate(pCharList) {
//Cluster
CharSample = (LABELEDLIST)first_node(pCharList);
Clusterer =
SetUpForClustering(FeatureDefs, CharSample, PROGRAM_FEATURE_TYPE);
if (Clusterer == NULL) { // To avoid a SIGSEGV
fprintf(stderr, "Error: NULL clusterer!\n");
return 1;
}
float SavedMinSamples = Config.MinSamples;
// To disable the tendency to produce a single cluster for all fonts,
// make MagicSamples an impossible to achieve number:
// Config.MagicSamples = CharSample->SampleCount * 10;
Config.MagicSamples = CharSample->SampleCount;
while (Config.MinSamples > 0.001) {
ProtoList = ClusterSamples(Clusterer, &Config);
if (NumberOfProtos(ProtoList, 1, 0) > 0) {
break;
} else {
Config.MinSamples *= 0.95;
printf("0 significant protos for %s."
" Retrying clustering with MinSamples = %f%%\n",
CharSample->Label, Config.MinSamples);
}
}
Config.MinSamples = SavedMinSamples;
AddToNormProtosList(&NormProtoList, ProtoList, CharSample->Label);
freeable_protos.push_back(ProtoList);
FreeClusterer(Clusterer);
}
FreeTrainingSamples(CharList);
int desc_index = ShortNameToFeatureType(FeatureDefs, PROGRAM_FEATURE_TYPE);
WriteNormProtos(FLAGS_D.c_str(), NormProtoList,
FeatureDefs.FeatureDesc[desc_index]);
FreeNormProtoList(NormProtoList);
for (int i = 0; i < freeable_protos.size(); ++i) {
FreeProtoList(&freeable_protos[i]);
}
printf ("\n");
return 0;
} // main
/*---------------------------------------------------------------------------*/
int main (
int argc,
char **argv)
/*
** Parameters:
** argc number of command line arguments
** argv array of command line arguments
** Globals: none
** Operation:
** This program reads in a text file consisting of feature
** samples from a training page in the following format:
**
** FontName CharName NumberOfFeatureTypes(N)
** FeatureTypeName1 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FeatureTypeName2 NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** ...
** FeatureTypeNameN NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** FontName CharName ...
**
** It then appends these samples into a separate file for each
** character. The name of the file is
**
** DirectoryName/FontName/CharName.FeatureTypeName
**
** The DirectoryName can be specified via a command
** line argument. If not specified, it defaults to the
** current directory. The format of the resulting files is:
**
** NumberOfFeatures(M)
** Feature1
** ...
** FeatureM
** NumberOfFeatures(M)
** ...
**
** The output files each have a header which describes the
** type of feature which the file contains. This header is
** in the format required by the clusterer. A command line
** argument can also be used to specify that only the first
** N samples of each class should be used.
** Return: none
** Exceptions: none
** History: Fri Aug 18 08:56:17 1989, DSJ, Created.
*/
{
char *PageName;
FILE *TrainingPage;
LIST CharList = NIL;
CLUSTERER *Clusterer = NULL;
LIST ProtoList = NIL;
LIST NormProtoList = NIL;
LIST pCharList;
LABELEDLIST CharSample;
ParseArguments (argc, argv);
while ((PageName = GetNextFilename()) != NULL)
{
printf ("Reading %s ...\n", PageName);
TrainingPage = Efopen (PageName, "r");
ReadTrainingSamples (TrainingPage, &CharList);
fclose (TrainingPage);
//WriteTrainingSamples (Directory, CharList);
}
printf("Clustering ...\n");
pCharList = CharList;
iterate(pCharList)
{
//Cluster
CharSample = (LABELEDLIST) first_node (pCharList);
//printf ("\nClustering %s ...", CharSample->Label);
Clusterer = SetUpForClustering(CharSample);
float SavedMinSamples = Config.MinSamples;
Config.MagicSamples = CharSample->SampleCount;
while (Config.MinSamples > 0.001) {
ProtoList = ClusterSamples(Clusterer, &Config);
if (NumberOfProtos(ProtoList, 1, 0) > 0)
break;
else {
Config.MinSamples *= 0.95;
printf("0 significant protos for %s."
" Retrying clustering with MinSamples = %f%%\n",
CharSample->Label, Config.MinSamples);
}
}
Config.MinSamples = SavedMinSamples;
AddToNormProtosList(&NormProtoList, ProtoList, CharSample->Label);
}
FreeTrainingSamples (CharList);
WriteNormProtos (Directory, NormProtoList, Clusterer);
FreeClusterer(Clusterer);
FreeProtoList(&ProtoList);
FreeNormProtoList(NormProtoList);
printf ("\n");
return 0;
} // main