// 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 "allheaders.h" #include "ccutil.h" #include "classify.h" #include "cluster.h" #include "clusttool.h" #include "efio.h" #include "emalloc.h" #include "featdefs.h" #include "fontinfo.h" #include "freelist.h" #include "globals.h" #include "intfeaturespace.h" #include "mastertrainer.h" #include "mf.h" #include "ndminx.h" #include "oldlist.h" #include "params.h" #include "shapetable.h" #include "tessdatamanager.h" #include "tessopt.h" #include "tprintf.h" #include "unicity_table.h" #include using tesseract::CCUtil; using tesseract::FontInfo; using tesseract::IntFeatureSpace; using tesseract::ParamUtils; using tesseract::ShapeTable; // Global Variables. // global variable to hold configuration parameters to control clustering // -M 0.625 -B 0.05 -I 1.0 -C 1e-6. CLUSTERCONFIG Config = { elliptical, 0.625, 0.05, 1.0, 1e-6, 0 }; FEATURE_DEFS_STRUCT feature_defs; CCUtil ccutil; INT_PARAM_FLAG(debug_level, 0, "Level of Trainer debugging"); INT_PARAM_FLAG(load_images, 0, "Load images with tr files"); STRING_PARAM_FLAG(configfile, "", "File to load more configs from"); STRING_PARAM_FLAG(D, "", "Directory to write output files to"); STRING_PARAM_FLAG(F, "font_properties", "File listing font properties"); STRING_PARAM_FLAG(X, "", "File listing font xheights"); STRING_PARAM_FLAG(U, "unicharset", "File to load unicharset from"); STRING_PARAM_FLAG(O, "", "File to write unicharset to"); STRING_PARAM_FLAG(T, "", "File to load trainer from"); STRING_PARAM_FLAG(output_trainer, "", "File to write trainer to"); STRING_PARAM_FLAG(test_ch, "", "UTF8 test character string"); DOUBLE_PARAM_FLAG(clusterconfig_min_samples_fraction, Config.MinSamples, "Min number of samples per proto as % of total"); DOUBLE_PARAM_FLAG(clusterconfig_max_illegal, Config.MaxIllegal, "Max percentage of samples in a cluster which have more" " than 1 feature in that cluster"); DOUBLE_PARAM_FLAG(clusterconfig_independence, Config.Independence, "Desired independence between dimensions"); DOUBLE_PARAM_FLAG(clusterconfig_confidence, Config.Confidence, "Desired confidence in prototypes created"); /** * This routine parses the command line arguments that were * passed to the program and ses them to set relevant * training-related global parameters * * Globals: * - Config current clustering parameters * @param argc number of command line arguments to parse * @param argv command line arguments * @return none * @note Exceptions: Illegal options terminate the program. */ void ParseArguments(int* argc, char ***argv) { STRING usage; if (*argc) { usage += (*argv)[0]; } usage += " [.tr files ...]"; tesseract::ParseCommandLineFlags(usage.c_str(), argc, argv, true); // Record the index of the first non-flag argument to 1, since we set // remove_flags to true when parsing the flags. tessoptind = 1; // Set some global values based on the flags. Config.MinSamples = MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_min_samples_fraction))); Config.MaxIllegal = MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_max_illegal))); Config.Independence = MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_independence))); Config.Confidence = MAX(0.0, MIN(1.0, double(FLAGS_clusterconfig_confidence))); // Set additional parameters from config file if specified. if (!FLAGS_configfile.empty()) { tesseract::ParamUtils::ReadParamsFile( FLAGS_configfile.c_str(), tesseract::SET_PARAM_CONSTRAINT_NON_INIT_ONLY, ccutil.params()); } } namespace tesseract { // Helper loads shape table from the given file. ShapeTable* LoadShapeTable(const STRING& file_prefix) { ShapeTable* shape_table = NULL; STRING shape_table_file = file_prefix; shape_table_file += kShapeTableFileSuffix; FILE* shape_fp = fopen(shape_table_file.string(), "rb"); if (shape_fp != NULL) { shape_table = new ShapeTable; if (!shape_table->DeSerialize(false, shape_fp)) { delete shape_table; shape_table = NULL; tprintf("Error: Failed to read shape table %s\n", shape_table_file.string()); } else { int num_shapes = shape_table->NumShapes(); tprintf("Read shape table %s of %d shapes\n", shape_table_file.string(), num_shapes); } fclose(shape_fp); } else { tprintf("Warning: No shape table file present: %s\n", shape_table_file.string()); } return shape_table; } // Helper to write the shape_table. void WriteShapeTable(const STRING& file_prefix, const ShapeTable& shape_table) { STRING shape_table_file = file_prefix; shape_table_file += kShapeTableFileSuffix; FILE* fp = fopen(shape_table_file.string(), "wb"); if (fp != NULL) { if (!shape_table.Serialize(fp)) { fprintf(stderr, "Error writing shape table: %s\n", shape_table_file.string()); } fclose(fp); } else { fprintf(stderr, "Error creating shape table: %s\n", shape_table_file.string()); } } /** * Creates a MasterTraininer and loads the training data into it: * Initializes feature_defs and IntegerFX. * Loads the shape_table if shape_table != NULL. * 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 NULL, 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 (NULL 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 != NULL) { *shape_table = LoadShapeTable(*file_prefix); if (*shape_table != NULL) 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 NULL; } } if (!FLAGS_X.empty()) { if (!trainer->LoadXHeights(FLAGS_X.c_str())) { delete trainer; return NULL; } } trainer->SetFeatureSpace(fs); const char* page_name; // Load training data from .tr files on the command line. while ((page_name = GetNextFilename(argc, argv)) != NULL) { 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 == NULL) { 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 == NULL) { 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 NULL; } 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 NULL; } if (shape_table != NULL) { // If we previously failed to load a shapetable, then shape clustering // wasn't run so make a flat one now. if (*shape_table == NULL) { *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; } } // namespace tesseract. /*---------------------------------------------------------------------------*/ /** * 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. * * Globals: * - tessoptind defined by tessopt sys call * @return Next command line argument or NULL. * @note Exceptions: none * @note History: Fri Aug 18 09:34:12 1989, DSJ, Created. */ const char *GetNextFilename(int argc, const char* const * argv) { if (tessoptind < argc) return argv[tessoptind++]; else return NULL; } /* GetNextFilename */ /*---------------------------------------------------------------------------*/ /** * This routine searches through a list of labeled lists to find * a list with the specified label. If a matching labeled list * cannot be found, NULL is returned. * @param List list to search * @param Label label to search for * @return Labeled list with the specified Label or NULL. * @note Globals: none * @note Exceptions: none * @note History: Fri Aug 18 15:57:41 1989, DSJ, Created. */ LABELEDLIST FindList ( LIST List, char *Label) { LABELEDLIST LabeledList; iterate (List) { LabeledList = (LABELEDLIST) first_node (List); if (strcmp (LabeledList->Label, Label) == 0) return (LabeledList); } return (NULL); } /* FindList */ /*---------------------------------------------------------------------------*/ /** * This routine allocates a new, empty labeled list and gives * it the specified label. * @param Label label for new list * @return New, empty labeled list. * @note Globals: none * @note Exceptions: none * @note History: Fri Aug 18 16:08:46 1989, DSJ, Created. */ LABELEDLIST NewLabeledList ( const char *Label) { 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 */ /*---------------------------------------------------------------------------*/ // TODO(rays) This is now used only by cntraining. Convert cntraining to use // the new method or get rid of it entirely. /** * 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. * @param file open text file to read samples from * @param feature_defs * @param feature_name * @param max_samples * @param unicharset * @param training_samples * @return none * @note Globals: none * @note Exceptions: none * @note 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. */ void ReadTrainingSamples(const FEATURE_DEFS_STRUCT& feature_defs, const char *feature_name, int max_samples, UNICHARSET* unicharset, FILE* file, LIST* training_samples) { char buffer[2048]; 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); // Zero out the font_sample_count for all the classes. LIST it = *training_samples; iterate(it) { char_sample = reinterpret_cast(first_node(it)); char_sample->font_sample_count = 0; } while (fgets(buffer, 2048, file) != NULL) { if (buffer[0] == '\n') continue; sscanf(buffer, "%*s %s", unichar); 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) { 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 /*---------------------------------------------------------------------------*/ /** * This routine deallocates all of the space allocated to * the specified list of training samples. * @param CharList list of all fonts in document * @return none * @note Globals: none * @note Exceptions: none * @note History: Fri Aug 18 17:44:27 1989, DSJ, Created. */ void FreeTrainingSamples(LIST CharList) { LABELEDLIST char_sample; FEATURE_SET FeatureSet; LIST FeatureList; iterate(CharList) { /* iterate through all of the fonts */ char_sample = (LABELEDLIST) first_node(CharList); FeatureList = char_sample->List; iterate(FeatureList) { /* iterate through all of the classes */ FeatureSet = (FEATURE_SET) first_node(FeatureList); FreeFeatureSet(FeatureSet); } FreeLabeledList(char_sample); } destroy(CharList); } /* FreeTrainingSamples */ /*---------------------------------------------------------------------------*/ /** * 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. * @param LabeledList labeled list to be freed * @note Globals: none * @return none * @note Exceptions: none * @note History: Fri Aug 18 17:52:45 1989, DSJ, Created. */ void FreeLabeledList(LABELEDLIST LabeledList) { destroy(LabeledList->List); free(LabeledList->Label); free(LabeledList); } /* FreeLabeledList */ /*---------------------------------------------------------------------------*/ /** * 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. * @param char_sample: LABELEDLIST that holds all the feature information for a * @param FeatureDefs * @param program_feature_type * given character. * @return Pointer to new clusterer data structure. * @note Globals: None * @note Exceptions: None * @note History: 8/16/89, DSJ, Created. */ CLUSTERER *SetUpForClustering(const FEATURE_DEFS_STRUCT &FeatureDefs, LABELEDLIST char_sample, const char* program_feature_type) { 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++) 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(FLAGS_test_ch.c_str(), 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, const 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 ( const 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 */ /*-----------------------------------------------------------------------------*/ /** * This routine deallocates all of the space allocated to * the specified list of training samples. * @param ClassList list of all fonts in document * @return none * @note Globals: none * @note Exceptions: none * @note History: Fri Aug 18 17:44:27 1989, DSJ, Created. */ void FreeLabeledClassList ( LIST ClassList) { MERGE_CLASS MergeClass; iterate (ClassList) /* iterate through all of the fonts */ { MergeClass = (MERGE_CLASS) first_node (ClassList); free (MergeClass->Label); FreeClass(MergeClass->Class); delete MergeClass; } destroy (ClassList); } /* FreeLabeledClassList */ /* SetUpForFloat2Int */ CLASS_STRUCT* 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"); CLASS_STRUCT* float_classes = new CLASS_STRUCT[unicharset.size()]; iterate(LabeledClassList) { UnicityTableEqEq font_set; MergeClass = (MERGE_CLASS) first_node (LabeledClassList); Class = &float_classes[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; } } return float_classes; } // 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 through 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); }