/**
* 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_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
/*---------------------------------------------------------------------------*/
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
