tesseract/training/cntraining.cpp
2017-01-25 16:20:19 -08:00

280 lines
9.5 KiB
C++

/******************************************************************************
** Filename: cntraining.cpp
** Purpose: Generates a normproto and pffmtable.
** Author: Dan Johnson
** Revisment: Christy Russon
** History: Fri Aug 18 08:53:50 1989, DSJ, Created.
** 5/25/90, DSJ, Adapted to multiple feature types.
** Tuesday, May 17, 1998 Changes made to make feature specific and
** simplify structures. First step in simplifying training process.
**
** (c) Copyright Hewlett-Packard Company, 1988.
** 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 Files and Type Defines
----------------------------------------------------------------------------*/
#include "oldlist.h"
#include "efio.h"
#include "emalloc.h"
#include "featdefs.h"
#include "tessopt.h"
#include "ocrfeatures.h"
#include "clusttool.h"
#include "cluster.h"
#include <string.h>
#include <stdio.h>
#include <math.h>
#include "unichar.h"
#include "commontraining.h"
#define PROGRAM_FEATURE_TYPE "cn"
DECLARE_STRING_PARAM_FLAG(D);
/*----------------------------------------------------------------------------
Public Function Prototypes
----------------------------------------------------------------------------*/
int main (
int argc,
char **argv);
/*----------------------------------------------------------------------------
Private Function Prototypes
----------------------------------------------------------------------------*/
void WriteNormProtos(const char *Directory, LIST LabeledProtoList,
const FEATURE_DESC_STRUCT *feature_desc);
/*
PARAMDESC *ConvertToPARAMDESC(
PARAM_DESC* Param_Desc,
int N);
*/
void WriteProtos(
FILE *File,
uinT16 N,
LIST ProtoList,
BOOL8 WriteSigProtos,
BOOL8 WriteInsigProtos);
/*----------------------------------------------------------------------------
Global Data Definitions and Declarations
----------------------------------------------------------------------------*/
/* global variable to hold configuration parameters to control clustering */
//-M 0.025 -B 0.05 -I 0.8 -C 1e-3
CLUSTERCONFIG CNConfig =
{
elliptical, 0.025, 0.05, 0.8, 1e-3, 0
};
/*----------------------------------------------------------------------------
Public Code
----------------------------------------------------------------------------*/
/*---------------------------------------------------------------------------*/
/**
* 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 = nullptr;
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)) != nullptr) {
printf("Reading %s ...\n", PageName);
TrainingPage = Efopen(PageName, "rb");
ReadTrainingSamples(FeatureDefs, PROGRAM_FEATURE_TYPE, 100, nullptr,
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 == nullptr) { // 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
/*----------------------------------------------------------------------------
Private Code
----------------------------------------------------------------------------*/
/*----------------------------------------------------------------------------*/
/**
* This routine writes the specified samples into files which
* are organized according to the font name and character name
* of the samples.
* @param Directory directory to place sample files into
* @param LabeledProtoList List of labeled protos
* @param feature_desc Description of the features
* @return none
* @note Exceptions: none
* @note History: Fri Aug 18 16:17:06 1989, DSJ, Created.
*/
void WriteNormProtos(const char *Directory, LIST LabeledProtoList,
const FEATURE_DESC_STRUCT *feature_desc) {
FILE *File;
STRING Filename;
LABELEDLIST LabeledProto;
int N;
Filename = "";
if (Directory != nullptr && Directory[0] != '\0') {
Filename += Directory;
Filename += "/";
}
Filename += "normproto";
printf ("\nWriting %s ...", Filename.string());
File = Efopen (Filename.string(), "wb");
fprintf(File, "%0d\n", feature_desc->NumParams);
WriteParamDesc(File, feature_desc->NumParams, feature_desc->ParamDesc);
iterate(LabeledProtoList)
{
LabeledProto = (LABELEDLIST) first_node (LabeledProtoList);
N = NumberOfProtos(LabeledProto->List, true, false);
if (N < 1) {
printf ("\nError! Not enough protos for %s: %d protos"
" (%d significant protos"
", %d insignificant protos)\n",
LabeledProto->Label, N,
NumberOfProtos(LabeledProto->List, 1, 0),
NumberOfProtos(LabeledProto->List, 0, 1));
exit(1);
}
fprintf(File, "\n%s %d\n", LabeledProto->Label, N);
WriteProtos(File, feature_desc->NumParams, LabeledProto->List, true, false);
}
fclose (File);
} // WriteNormProtos
/*-------------------------------------------------------------------------*/
void WriteProtos(
FILE *File,
uinT16 N,
LIST ProtoList,
BOOL8 WriteSigProtos,
BOOL8 WriteInsigProtos)
{
PROTOTYPE *Proto;
// write prototypes
iterate(ProtoList)
{
Proto = (PROTOTYPE *) first_node ( ProtoList );
if (( Proto->Significant && WriteSigProtos ) ||
( ! Proto->Significant && WriteInsigProtos ) )
WritePrototype( File, N, Proto );
}
} // WriteProtos