/****************************************************************************** ** 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 #include #include #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 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