Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/training/common/mastertrainer.h @ 17:dd9cdb856310
Remove PKG-INFO from the because it is regenerated automatically for the sdist
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Thu, 18 Sep 2025 17:40:40 +0200 |
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| rev | line source |
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1 // Copyright 2010 Google Inc. All Rights Reserved. |
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2 // Author: rays@google.com (Ray Smith) |
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3 /////////////////////////////////////////////////////////////////////// |
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4 // File: mastertrainer.h |
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5 // Description: Trainer to build the MasterClassifier. |
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6 // Author: Ray Smith |
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7 // |
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8 // (C) Copyright 2010, Google Inc. |
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9 // Licensed under the Apache License, Version 2.0 (the "License"); |
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10 // you may not use this file except in compliance with the License. |
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11 // You may obtain a copy of the License at |
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12 // http://www.apache.org/licenses/LICENSE-2.0 |
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13 // Unless required by applicable law or agreed to in writing, software |
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14 // distributed under the License is distributed on an "AS IS" BASIS, |
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15 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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16 // See the License for the specific language governing permissions and |
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17 // limitations under the License. |
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18 // |
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19 /////////////////////////////////////////////////////////////////////// |
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20 |
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21 #ifndef TESSERACT_TRAINING_MASTERTRAINER_H_ |
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22 #define TESSERACT_TRAINING_MASTERTRAINER_H_ |
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23 |
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24 #include "export.h" |
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25 |
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26 #include "classify.h" |
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27 #include "cluster.h" |
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28 #include "elst.h" |
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29 #include "errorcounter.h" |
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30 #include "featdefs.h" |
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31 #include "fontinfo.h" |
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32 #include "indexmapbidi.h" |
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33 #include "intfeaturemap.h" |
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34 #include "intfeaturespace.h" |
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35 #include "intfx.h" |
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36 #include "intmatcher.h" |
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37 #include "params.h" |
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38 #include "shapetable.h" |
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39 #include "trainingsample.h" |
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40 #include "trainingsampleset.h" |
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41 #include "unicharset.h" |
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42 |
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43 namespace tesseract { |
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44 |
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45 class ShapeClassifier; |
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46 |
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47 // Simple struct to hold the distance between two shapes during clustering. |
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48 struct ShapeDist { |
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49 ShapeDist() : shape1(0), shape2(0), distance(0.0f) {} |
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50 ShapeDist(int s1, int s2, float dist) : shape1(s1), shape2(s2), distance(dist) {} |
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51 |
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52 // Sort operator to sort in ascending order of distance. |
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53 bool operator<(const ShapeDist &other) const { |
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54 return distance < other.distance; |
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55 } |
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56 |
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57 int shape1; |
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58 int shape2; |
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59 float distance; |
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60 }; |
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61 |
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62 // Class to encapsulate training processes that use the TrainingSampleSet. |
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63 // Initially supports shape clustering and mftrainining. |
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64 // Other important features of the MasterTrainer are conditioning the data |
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65 // by outlier elimination, replication with perturbation, and serialization. |
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66 class TESS_COMMON_TRAINING_API MasterTrainer { |
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67 public: |
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68 MasterTrainer(NormalizationMode norm_mode, bool shape_analysis, bool replicate_samples, |
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69 int debug_level); |
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70 ~MasterTrainer(); |
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71 |
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72 // Writes to the given file. Returns false in case of error. |
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73 bool Serialize(FILE *fp) const; |
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74 |
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75 // Loads an initial unicharset, or sets one up if the file cannot be read. |
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76 void LoadUnicharset(const char *filename); |
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77 |
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78 // Sets the feature space definition. |
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79 void SetFeatureSpace(const IntFeatureSpace &fs) { |
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80 feature_space_ = fs; |
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81 feature_map_.Init(fs); |
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82 } |
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83 |
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84 // Reads the samples and their features from the given file, |
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85 // adding them to the trainer with the font_id from the content of the file. |
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86 // If verification, then these are verification samples, not training. |
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87 void ReadTrainingSamples(const char *page_name, const FEATURE_DEFS_STRUCT &feature_defs, |
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88 bool verification); |
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89 |
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90 // Adds the given single sample to the trainer, setting the classid |
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91 // appropriately from the given unichar_str. |
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92 void AddSample(bool verification, const char *unichar_str, TrainingSample *sample); |
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93 |
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94 // Loads all pages from the given tif filename and append to page_images_. |
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95 // Must be called after ReadTrainingSamples, as the current number of images |
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96 // is used as an offset for page numbers in the samples. |
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97 void LoadPageImages(const char *filename); |
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98 |
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99 // Cleans up the samples after initial load from the tr files, and prior to |
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100 // saving the MasterTrainer: |
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101 // Remaps fragmented chars if running shape analysis. |
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102 // Sets up the samples appropriately for class/fontwise access. |
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103 // Deletes outlier samples. |
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104 void PostLoadCleanup(); |
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105 |
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106 // Gets the samples ready for training. Use after both |
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107 // ReadTrainingSamples+PostLoadCleanup or DeSerialize. |
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108 // Re-indexes the features and computes canonical and cloud features. |
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109 void PreTrainingSetup(); |
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110 |
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111 // Sets up the master_shapes_ table, which tells which fonts should stay |
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112 // together until they get to a leaf node classifier. |
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113 void SetupMasterShapes(); |
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114 |
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115 // Adds the junk_samples_ to the main samples_ set. Junk samples are initially |
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116 // fragments and n-grams (all incorrectly segmented characters). |
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117 // Various training functions may result in incorrectly segmented characters |
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118 // being added to the unicharset of the main samples, perhaps because they |
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119 // form a "radical" decomposition of some (Indic) grapheme, or because they |
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120 // just look the same as a real character (like rn/m) |
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121 // This function moves all the junk samples, to the main samples_ set, but |
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122 // desirable junk, being any sample for which the unichar already exists in |
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123 // the samples_ unicharset gets the unichar-ids re-indexed to match, but |
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124 // anything else gets re-marked as unichar_id 0 (space character) to identify |
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125 // it as junk to the error counter. |
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126 void IncludeJunk(); |
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127 |
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128 // Replicates the samples and perturbs them if the enable_replication_ flag |
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129 // is set. MUST be used after the last call to OrganizeByFontAndClass on |
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130 // the training samples, ie after IncludeJunk if it is going to be used, as |
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131 // OrganizeByFontAndClass will eat the replicated samples into the regular |
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132 // samples. |
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133 void ReplicateAndRandomizeSamplesIfRequired(); |
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134 |
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135 // Loads the basic font properties file into fontinfo_table_. |
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136 // Returns false on failure. |
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137 bool LoadFontInfo(const char *filename); |
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138 |
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139 // Loads the xheight font properties file into xheights_. |
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140 // Returns false on failure. |
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141 bool LoadXHeights(const char *filename); |
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142 |
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143 // Reads spacing stats from filename and adds them to fontinfo_table. |
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144 // Returns false on failure. |
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145 bool AddSpacingInfo(const char *filename); |
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146 |
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147 // Returns the font id corresponding to the given font name. |
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148 // Returns -1 if the font cannot be found. |
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149 int GetFontInfoId(const char *font_name); |
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150 // Returns the font_id of the closest matching font name to the given |
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151 // filename. It is assumed that a substring of the filename will match |
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152 // one of the fonts. If more than one is matched, the longest is returned. |
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153 int GetBestMatchingFontInfoId(const char *filename); |
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154 |
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155 // Returns the filename of the tr file corresponding to the command-line |
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156 // argument with the given index. |
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157 const std::string &GetTRFileName(int index) const { |
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158 return tr_filenames_[index]; |
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159 } |
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160 |
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161 // Sets up a flat shapetable with one shape per class/font combination. |
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162 void SetupFlatShapeTable(ShapeTable *shape_table); |
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163 |
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164 // Sets up a Clusterer for mftraining on a single shape_id. |
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165 // Call FreeClusterer on the return value after use. |
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166 CLUSTERER *SetupForClustering(const ShapeTable &shape_table, |
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167 const FEATURE_DEFS_STRUCT &feature_defs, int shape_id, |
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168 int *num_samples); |
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169 |
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170 // Writes the given float_classes (produced by SetupForFloat2Int) as inttemp |
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171 // to the given inttemp_file, and the corresponding pffmtable. |
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172 // The unicharset is the original encoding of graphemes, and shape_set should |
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173 // match the size of the shape_table, and may possibly be totally fake. |
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174 void WriteInttempAndPFFMTable(const UNICHARSET &unicharset, const UNICHARSET &shape_set, |
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175 const ShapeTable &shape_table, CLASS_STRUCT *float_classes, |
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176 const char *inttemp_file, const char *pffmtable_file); |
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177 |
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178 const UNICHARSET &unicharset() const { |
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179 return samples_.unicharset(); |
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180 } |
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181 TrainingSampleSet *GetSamples() { |
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182 return &samples_; |
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183 } |
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184 const ShapeTable &master_shapes() const { |
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185 return master_shapes_; |
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186 } |
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187 |
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188 // Generates debug output relating to the canonical distance between the |
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189 // two given UTF8 grapheme strings. |
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190 void DebugCanonical(const char *unichar_str1, const char *unichar_str2); |
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191 #ifndef GRAPHICS_DISABLED |
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192 // Debugging for cloud/canonical features. |
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193 // Displays a Features window containing: |
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194 // If unichar_str2 is in the unicharset, and canonical_font is non-negative, |
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195 // displays the canonical features of the char/font combination in red. |
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196 // If unichar_str1 is in the unicharset, and cloud_font is non-negative, |
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197 // displays the cloud feature of the char/font combination in green. |
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198 // The canonical features are drawn first to show which ones have no |
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199 // matches in the cloud features. |
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200 // Until the features window is destroyed, each click in the features window |
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201 // will display the samples that have that feature in a separate window. |
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202 void DisplaySamples(const char *unichar_str1, int cloud_font, const char *unichar_str2, |
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203 int canonical_font); |
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204 #endif // !GRAPHICS_DISABLED |
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205 |
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206 void TestClassifierVOld(bool replicate_samples, ShapeClassifier *test_classifier, |
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207 ShapeClassifier *old_classifier); |
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208 |
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209 // Tests the given test_classifier on the internal samples. |
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210 // See TestClassifier for details. |
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211 void TestClassifierOnSamples(CountTypes error_mode, int report_level, bool replicate_samples, |
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212 ShapeClassifier *test_classifier, std::string *report_string); |
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213 // Tests the given test_classifier on the given samples |
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214 // error_mode indicates what counts as an error. |
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215 // report_levels: |
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216 // 0 = no output. |
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217 // 1 = bottom-line error rate. |
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218 // 2 = bottom-line error rate + time. |
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219 // 3 = font-level error rate + time. |
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220 // 4 = list of all errors + short classifier debug output on 16 errors. |
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221 // 5 = list of all errors + short classifier debug output on 25 errors. |
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222 // If replicate_samples is true, then the test is run on an extended test |
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223 // sample including replicated and systematically perturbed samples. |
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224 // If report_string is non-nullptr, a summary of the results for each font |
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225 // is appended to the report_string. |
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226 double TestClassifier(CountTypes error_mode, int report_level, bool replicate_samples, |
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227 TrainingSampleSet *samples, ShapeClassifier *test_classifier, |
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228 std::string *report_string); |
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229 |
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230 // Returns the average (in some sense) distance between the two given |
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231 // shapes, which may contain multiple fonts and/or unichars. |
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232 // This function is public to facilitate testing. |
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233 float ShapeDistance(const ShapeTable &shapes, int s1, int s2); |
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234 |
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235 private: |
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236 // Replaces samples that are always fragmented with the corresponding |
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237 // fragment samples. |
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238 void ReplaceFragmentedSamples(); |
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239 |
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240 // Runs a hierarchical agglomerative clustering to merge shapes in the given |
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241 // shape_table, while satisfying the given constraints: |
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242 // * End with at least min_shapes left in shape_table, |
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243 // * No shape shall have more than max_shape_unichars in it, |
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244 // * Don't merge shapes where the distance between them exceeds max_dist. |
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245 void ClusterShapes(int min_shapes, int max_shape_unichars, float max_dist, |
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246 ShapeTable *shape_table); |
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247 |
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248 private: |
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249 NormalizationMode norm_mode_; |
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250 // Character set we are training for. |
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251 UNICHARSET unicharset_; |
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252 // Original feature space. Subspace mapping is contained in feature_map_. |
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253 IntFeatureSpace feature_space_; |
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254 TrainingSampleSet samples_; |
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255 TrainingSampleSet junk_samples_; |
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256 TrainingSampleSet verify_samples_; |
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257 // Master shape table defines what fonts stay together until the leaves. |
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258 ShapeTable master_shapes_; |
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259 // Flat shape table has each unichar/font id pair in a separate shape. |
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260 ShapeTable flat_shapes_; |
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261 // Font metrics gathered from multiple files. |
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262 FontInfoTable fontinfo_table_; |
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263 // Array of xheights indexed by font ids in fontinfo_table_; |
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264 std::vector<int32_t> xheights_; |
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265 |
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266 // Non-serialized data initialized by other means or used temporarily |
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267 // during loading of training samples. |
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268 // Number of different class labels in unicharset_. |
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269 int charsetsize_; |
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270 // Flag to indicate that we are running shape analysis and need fragments |
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271 // fixing. |
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272 bool enable_shape_analysis_; |
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273 // Flag to indicate that sample replication is required. |
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274 bool enable_replication_; |
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275 // Array of classids of fragments that replace the correctly segmented chars. |
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276 int *fragments_; |
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277 // Classid of previous correctly segmented sample that was added. |
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278 int prev_unichar_id_; |
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279 // Debug output control. |
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280 int debug_level_; |
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281 // Feature map used to construct reduced feature spaces for compact |
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282 // classifiers. |
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283 IntFeatureMap feature_map_; |
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284 // Vector of Pix pointers used for classifiers that need the image. |
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285 // Indexed by page_num_ in the samples. |
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286 // These images are owned by the trainer and need to be pixDestroyed. |
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287 std::vector<Image > page_images_; |
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288 // Vector of filenames of loaded tr files. |
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289 std::vector<std::string> tr_filenames_; |
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290 }; |
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291 |
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292 } // namespace tesseract. |
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293 |
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294 #endif // TESSERACT_TRAINING_MASTERTRAINER_H_ |
