Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/training/mftraining.cpp @ 17:dd9cdb856310
Remove PKG-INFO from the because it is regenerated automatically for the sdist
| author | Franz Glasner <fzglas.hg@dom66.de> |
|---|---|
| date | Thu, 18 Sep 2025 17:40:40 +0200 |
| parents | b50eed0cc0ef |
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| rev | line source |
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1 /****************************************************************************** |
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2 ** Filename: mftraining.c |
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3 ** Purpose: Separates training pages into files for each character. |
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4 ** Strips from files only the features and there parameters of |
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5 ** the feature type mf. |
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6 ** Author: Dan Johnson |
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7 ** Revisment: Christy Russon |
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8 ** |
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9 ** (c) Copyright Hewlett-Packard Company, 1988. |
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10 ** Licensed under the Apache License, Version 2.0 (the "License"); |
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11 ** you may not use this file except in compliance with the License. |
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12 ** You may obtain a copy of the License at |
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13 ** http://www.apache.org/licenses/LICENSE-2.0 |
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14 ** Unless required by applicable law or agreed to in writing, software |
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15 ** distributed under the License is distributed on an "AS IS" BASIS, |
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16 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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17 ** See the License for the specific language governing permissions and |
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18 ** limitations under the License. |
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19 ******************************************************************************/ |
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20 /*---------------------------------------------------------------------------- |
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21 Include Files and Type Defines |
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22 ----------------------------------------------------------------------------*/ |
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23 |
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24 #define _USE_MATH_DEFINES // for M_PI |
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25 #ifdef HAVE_CONFIG_H |
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26 # include "config_auto.h" |
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27 #endif |
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28 |
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29 #include <cmath> // for M_PI |
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30 #include <cstdio> |
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31 #include <cstring> |
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32 |
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33 #include "classify.h" |
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34 #include "cluster.h" |
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35 #include "clusttool.h" |
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36 #include "commontraining.h" |
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37 #include "featdefs.h" |
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38 #include "fontinfo.h" |
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39 #include "indexmapbidi.h" |
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40 #include "intproto.h" |
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41 #include "mastertrainer.h" |
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42 #include "mergenf.h" |
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43 #include "mf.h" |
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44 #include "ocrfeatures.h" |
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45 #include "oldlist.h" |
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46 #include "protos.h" |
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47 #include "shapetable.h" |
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48 #include "tprintf.h" |
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49 #include "unicity_table.h" |
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50 |
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51 using namespace tesseract; |
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52 |
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53 /*---------------------------------------------------------------------------- |
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54 Public Code |
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55 -----------------------------------------------------------------------------*/ |
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56 #ifndef GRAPHICS_DISABLED |
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57 static void DisplayProtoList(const char *ch, LIST protolist) { |
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58 auto window = std::make_unique<ScrollView>("Char samples", 50, 200, 520, 520, 260, 260, true); |
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59 LIST proto = protolist; |
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60 iterate(proto) { |
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61 auto *prototype = reinterpret_cast<PROTOTYPE *>(proto->first_node()); |
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62 if (prototype->Significant) { |
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63 window->Pen(ScrollView::GREEN); |
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64 } else if (prototype->NumSamples == 0) { |
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65 window->Pen(ScrollView::BLUE); |
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66 } else if (prototype->Merged) { |
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67 window->Pen(ScrollView::MAGENTA); |
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68 } else { |
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69 window->Pen(ScrollView::RED); |
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70 } |
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71 float x = CenterX(prototype->Mean); |
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72 float y = CenterY(prototype->Mean); |
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73 double angle = OrientationOf(prototype->Mean) * 2 * M_PI; |
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74 auto dx = static_cast<float>(LengthOf(prototype->Mean) * cos(angle) / 2); |
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75 auto dy = static_cast<float>(LengthOf(prototype->Mean) * sin(angle) / 2); |
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76 window->SetCursor((x - dx) * 256, (y - dy) * 256); |
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77 window->DrawTo((x + dx) * 256, (y + dy) * 256); |
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78 auto prototypeNumSamples = prototype->NumSamples; |
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79 if (prototype->Significant) { |
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80 tprintf("Green proto at (%g,%g)+(%g,%g) %d samples\n", x, y, dx, dy, prototypeNumSamples); |
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81 } else if (prototype->NumSamples > 0 && !prototype->Merged) { |
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82 tprintf("Red proto at (%g,%g)+(%g,%g) %d samples\n", x, y, dx, dy, prototypeNumSamples); |
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83 } |
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84 } |
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85 window->Update(); |
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86 } |
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87 #endif // !GRAPHICS_DISABLED |
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88 |
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89 // Helper to run clustering on a single config. |
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90 // Mostly copied from the old mftraining, but with renamed variables. |
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91 static LIST ClusterOneConfig(int shape_id, const char *class_label, LIST mf_classes, |
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92 const ShapeTable &shape_table, MasterTrainer *trainer) { |
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93 int num_samples; |
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94 CLUSTERER *clusterer = |
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95 trainer->SetupForClustering(shape_table, feature_defs, shape_id, &num_samples); |
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96 Config.MagicSamples = num_samples; |
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97 LIST proto_list = ClusterSamples(clusterer, &Config); |
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98 CleanUpUnusedData(proto_list); |
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99 |
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100 // Merge protos where reasonable to make more of them significant by |
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101 // representing almost all samples of the class/font. |
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102 MergeInsignificantProtos(proto_list, class_label, clusterer, &Config); |
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103 #ifndef GRAPHICS_DISABLED |
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104 if (strcmp(FLAGS_test_ch.c_str(), class_label) == 0) { |
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105 DisplayProtoList(FLAGS_test_ch.c_str(), proto_list); |
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106 } |
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107 #endif // !GRAPHICS_DISABLED |
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108 // Delete the protos that will not be used in the inttemp output file. |
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109 proto_list = RemoveInsignificantProtos(proto_list, true, false, clusterer->SampleSize); |
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110 FreeClusterer(clusterer); |
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111 MERGE_CLASS merge_class = FindClass(mf_classes, class_label); |
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112 if (merge_class == nullptr) { |
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113 merge_class = new MERGE_CLASS_NODE(class_label); |
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114 mf_classes = push(mf_classes, merge_class); |
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115 } |
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116 int config_id = AddConfigToClass(merge_class->Class); |
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117 merge_class->Class->font_set.push_back(shape_id); |
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118 LIST proto_it = proto_list; |
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119 iterate(proto_it) { |
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120 auto *prototype = reinterpret_cast<PROTOTYPE *>(proto_it->first_node()); |
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121 // See if proto can be approximated by existing proto. |
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122 int p_id = FindClosestExistingProto(merge_class->Class, merge_class->NumMerged, prototype); |
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123 if (p_id == NO_PROTO) { |
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124 // Need to make a new proto, as it doesn't match anything. |
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125 p_id = AddProtoToClass(merge_class->Class); |
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126 MakeNewFromOld(ProtoIn(merge_class->Class, p_id), prototype); |
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127 merge_class->NumMerged[p_id] = 1; |
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128 } else { |
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129 PROTO_STRUCT dummy_proto; |
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130 MakeNewFromOld(&dummy_proto, prototype); |
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131 // Merge with the similar proto. |
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132 ComputeMergedProto(ProtoIn(merge_class->Class, p_id), &dummy_proto, |
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133 static_cast<float>(merge_class->NumMerged[p_id]), 1.0, |
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134 ProtoIn(merge_class->Class, p_id)); |
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135 merge_class->NumMerged[p_id]++; |
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136 } |
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137 AddProtoToConfig(p_id, merge_class->Class->Configurations[config_id]); |
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138 } |
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139 FreeProtoList(&proto_list); |
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140 return mf_classes; |
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141 } |
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142 |
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143 // Helper to setup the config map. |
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144 // Setup an index mapping from the shapes in the shape table to the classes |
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145 // that will be trained. In keeping with the original design, each shape |
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146 // with the same list of unichars becomes a different class and the configs |
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147 // represent the different combinations of fonts. |
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148 static void SetupConfigMap(ShapeTable *shape_table, IndexMapBiDi *config_map) { |
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149 int num_configs = shape_table->NumShapes(); |
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150 config_map->Init(num_configs, true); |
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151 config_map->Setup(); |
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152 for (int c1 = 0; c1 < num_configs; ++c1) { |
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153 // Only process ids that are not already merged. |
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154 if (config_map->SparseToCompact(c1) == c1) { |
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155 Shape *shape1 = shape_table->MutableShape(c1); |
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156 // Find all the subsequent shapes that are equal. |
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157 for (int c2 = c1 + 1; c2 < num_configs; ++c2) { |
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158 if (shape_table->MutableShape(c2)->IsEqualUnichars(shape1)) { |
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159 config_map->Merge(c1, c2); |
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160 } |
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161 } |
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162 } |
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163 } |
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164 config_map->CompleteMerges(); |
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165 } |
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166 |
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167 /** |
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168 * This program reads in a text file consisting of feature |
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169 * samples from a training page in the following format: |
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170 * @verbatim |
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171 FontName UTF8-char-str xmin ymin xmax ymax page-number |
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172 NumberOfFeatureTypes(N) |
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173 FeatureTypeName1 NumberOfFeatures(M) |
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174 Feature1 |
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175 ... |
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176 FeatureM |
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177 FeatureTypeName2 NumberOfFeatures(M) |
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178 Feature1 |
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179 ... |
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180 FeatureM |
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181 ... |
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182 FeatureTypeNameN NumberOfFeatures(M) |
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183 Feature1 |
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184 ... |
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185 FeatureM |
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186 FontName CharName ... |
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187 @endverbatim |
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188 * The result of this program is a binary inttemp file used by |
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189 * the OCR engine. |
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190 * @param argc number of command line arguments |
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191 * @param argv array of command line arguments |
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192 * @return 0 if no error occurred |
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193 */ |
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194 int main(int argc, char **argv) { |
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195 tesseract::CheckSharedLibraryVersion(); |
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196 |
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197 ParseArguments(&argc, &argv); |
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198 |
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199 ShapeTable *shape_table = nullptr; |
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200 std::string file_prefix; |
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201 // Load the training data. |
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202 auto trainer = tesseract::LoadTrainingData(argv + 1, false, &shape_table, file_prefix); |
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203 if (trainer == nullptr) { |
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204 return EXIT_FAILURE; // Failed. |
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205 } |
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206 |
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207 // Setup an index mapping from the shapes in the shape table to the classes |
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208 // that will be trained. In keeping with the original design, each shape |
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209 // with the same list of unichars becomes a different class and the configs |
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210 // represent the different combinations of fonts. |
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211 IndexMapBiDi config_map; |
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212 SetupConfigMap(shape_table, &config_map); |
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213 |
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214 WriteShapeTable(file_prefix, *shape_table); |
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215 // If the shape_table is flat, then either we didn't run shape clustering, or |
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216 // it did nothing, so we just output the trainer's unicharset. |
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217 // Otherwise shape_set will hold a fake unicharset with an entry for each |
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218 // shape in the shape table, and we will output that instead. |
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219 UNICHARSET shape_set; |
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220 const UNICHARSET *unicharset = &trainer->unicharset(); |
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221 // If we ran shapeclustering (and it worked) then at least one shape will |
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222 // have multiple unichars, so we have to build a fake unicharset. |
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223 if (shape_table->AnyMultipleUnichars()) { |
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224 unicharset = &shape_set; |
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225 // Now build a fake unicharset for the compact shape space to keep the |
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226 // output modules happy that we are doing things correctly. |
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227 int num_shapes = config_map.CompactSize(); |
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228 for (int s = 0; s < num_shapes; ++s) { |
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229 char shape_label[14]; |
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230 snprintf(shape_label, sizeof(shape_label), "sh%04d", s); |
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231 shape_set.unichar_insert(shape_label); |
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232 } |
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233 } |
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234 |
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235 // Now train each config separately. |
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236 int num_configs = shape_table->NumShapes(); |
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237 LIST mf_classes = NIL_LIST; |
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238 for (int s = 0; s < num_configs; ++s) { |
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239 int unichar_id, font_id; |
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240 if (unicharset == &shape_set) { |
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241 // Using fake unichar_ids from the config_map/shape_set. |
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242 unichar_id = config_map.SparseToCompact(s); |
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243 } else { |
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244 // Get the real unichar_id from the shape table/unicharset. |
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245 shape_table->GetFirstUnicharAndFont(s, &unichar_id, &font_id); |
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246 } |
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247 const char *class_label = unicharset->id_to_unichar(unichar_id); |
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248 mf_classes = ClusterOneConfig(s, class_label, mf_classes, *shape_table, trainer.get()); |
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249 } |
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250 std::string inttemp_file = file_prefix; |
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251 inttemp_file += "inttemp"; |
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252 std::string pffmtable_file = std::move(file_prefix); |
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253 pffmtable_file += "pffmtable"; |
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254 CLASS_STRUCT *float_classes = SetUpForFloat2Int(*unicharset, mf_classes); |
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255 // Now write the inttemp and pffmtable. |
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256 trainer->WriteInttempAndPFFMTable(trainer->unicharset(), *unicharset, *shape_table, float_classes, |
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257 inttemp_file.c_str(), pffmtable_file.c_str()); |
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258 for (size_t c = 0; c < unicharset->size(); ++c) { |
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259 FreeClassFields(&float_classes[c]); |
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260 } |
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261 delete[] float_classes; |
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262 FreeLabeledClassList(mf_classes); |
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263 delete shape_table; |
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264 printf("Done!\n"); |
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265 if (!FLAGS_test_ch.empty()) { |
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266 // If we are displaying debug window(s), wait for the user to look at them. |
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267 printf("Hit return to exit...\n"); |
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268 while (getchar() != '\n') { |
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269 ; |
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270 } |
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271 } |
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272 return EXIT_SUCCESS; |
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273 } /* main */ |
