Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/training/common/trainingsampleset.h @ 46:7ee69f120f19 default tip

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>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 17:17:30 +0200
parents b50eed0cc0ef
children
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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 // Licensed under the Apache License, Version 2.0 (the "License");
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5 // you may not use this file except in compliance with the License.
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6 // You may obtain a copy of the License at
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7 // http://www.apache.org/licenses/LICENSE-2.0
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8 // Unless required by applicable law or agreed to in writing, software
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9 // distributed under the License is distributed on an "AS IS" BASIS,
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10 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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11 // See the License for the specific language governing permissions and
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12 // limitations under the License.
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13 //
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14 ///////////////////////////////////////////////////////////////////////
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16 #ifndef TESSERACT_TRAINING_TRAININGSAMPLESET_H_
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17 #define TESSERACT_TRAINING_TRAININGSAMPLESET_H_
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19 #include "bitvector.h"
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20 #include "indexmapbidi.h"
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21 #include "matrix.h"
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22 #include "shapetable.h"
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23 #include "trainingsample.h"
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25 namespace tesseract {
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27 class UNICHARSET;
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28 struct FontInfo;
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29 class FontInfoTable;
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30 class IntFeatureMap;
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31 class IntFeatureSpace;
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32 class TrainingSample;
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33 struct UnicharAndFonts;
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34
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35 // Collection of TrainingSample used for training or testing a classifier.
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36 // Provides several useful methods to operate on the collection as a whole,
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37 // including outlier detection and deletion, providing access by font and
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38 // class, finding the canonical sample, finding the "cloud" features (OR of
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39 // all features in all samples), replication of samples, caching of distance
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40 // metrics.
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41 class TrainingSampleSet {
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42 public:
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43 explicit TrainingSampleSet(const FontInfoTable &fontinfo_table);
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44 ~TrainingSampleSet();
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45
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46 // Writes to the given file. Returns false in case of error.
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47 bool Serialize(FILE *fp) const;
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48 // Reads from the given file. Returns false in case of error.
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49 // If swap is true, assumes a big/little-endian swap is needed.
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50 bool DeSerialize(bool swap, FILE *fp);
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51
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52 // Accessors
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53 int num_samples() const {
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54 return samples_.size();
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55 }
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56 int num_raw_samples() const {
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57 return num_raw_samples_;
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58 }
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59 int NumFonts() const {
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60 return font_id_map_.SparseSize();
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61 }
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62 const UNICHARSET &unicharset() const {
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63 return unicharset_;
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64 }
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65 int charsetsize() const {
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66 return unicharset_size_;
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67 }
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68 const FontInfoTable &fontinfo_table() const {
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69 return fontinfo_table_;
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70 }
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71
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72 // Loads an initial unicharset, or sets one up if the file cannot be read.
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73 void LoadUnicharset(const char *filename);
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74
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75 // Adds a character sample to this sample set.
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76 // If the unichar is not already in the local unicharset, it is added.
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77 // Returns the unichar_id of the added sample, from the local unicharset.
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78 int AddSample(const char *unichar, TrainingSample *sample);
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79 // Adds a character sample to this sample set with the given unichar_id,
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80 // which must correspond to the local unicharset (in this).
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81 void AddSample(int unichar_id, TrainingSample *sample);
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82
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83 // Returns the number of samples for the given font,class pair.
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84 // If randomize is true, returns the number of samples accessible
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85 // with randomizing on. (Increases the number of samples if small.)
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86 // OrganizeByFontAndClass must have been already called.
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87 int NumClassSamples(int font_id, int class_id, bool randomize) const;
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88
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89 // Gets a sample by its index.
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90 const TrainingSample *GetSample(int index) const;
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91
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92 // Gets a sample by its font, class, index.
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93 // OrganizeByFontAndClass must have been already called.
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94 const TrainingSample *GetSample(int font_id, int class_id, int index) const;
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95
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96 // Get a sample by its font, class, index. Does not randomize.
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97 // OrganizeByFontAndClass must have been already called.
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98 TrainingSample *MutableSample(int font_id, int class_id, int index);
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99
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100 // Returns a string debug representation of the given sample:
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101 // font, unichar_str, bounding box, page.
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102 std::string SampleToString(const TrainingSample &sample) const;
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103
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104 // Gets the combined set of features used by all the samples of the given
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105 // font/class combination.
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106 const BitVector &GetCloudFeatures(int font_id, int class_id) const;
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107 // Gets the indexed features of the canonical sample of the given
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108 // font/class combination.
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109 const std::vector<int> &GetCanonicalFeatures(int font_id, int class_id) const;
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110
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111 // Returns the distance between the given UniCharAndFonts pair.
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112 // If matched_fonts, only matching fonts, are considered, unless that yields
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113 // the empty set.
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114 // OrganizeByFontAndClass must have been already called.
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115 float UnicharDistance(const UnicharAndFonts &uf1, const UnicharAndFonts &uf2, bool matched_fonts,
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116 const IntFeatureMap &feature_map);
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117
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118 // Returns the distance between the given pair of font/class pairs.
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119 // Finds in cache or computes and caches.
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120 // OrganizeByFontAndClass must have been already called.
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121 float ClusterDistance(int font_id1, int class_id1, int font_id2, int class_id2,
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122 const IntFeatureMap &feature_map);
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123
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124 // Computes the distance between the given pair of font/class pairs.
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125 float ComputeClusterDistance(int font_id1, int class_id1, int font_id2, int class_id2,
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126 const IntFeatureMap &feature_map) const;
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127
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128 // Returns the number of canonical features of font/class 2 for which
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129 // neither the feature nor any of its near neighbors occurs in the cloud
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130 // of font/class 1. Each such feature is a reliable separation between
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131 // the classes, ASSUMING that the canonical sample is sufficiently
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132 // representative that every sample has a feature near that particular
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133 // feature. To check that this is so on the fly would be prohibitively
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134 // expensive, but it might be possible to pre-qualify the canonical features
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135 // to include only those for which this assumption is true.
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136 // ComputeCanonicalFeatures and ComputeCloudFeatures must have been called
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137 // first, or the results will be nonsense.
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138 int ReliablySeparable(int font_id1, int class_id1, int font_id2, int class_id2,
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139 const IntFeatureMap &feature_map, bool thorough) const;
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140
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141 // Returns the total index of the requested sample.
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142 // OrganizeByFontAndClass must have been already called.
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143 int GlobalSampleIndex(int font_id, int class_id, int index) const;
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144
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145 // Gets the canonical sample for the given font, class pair.
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146 // ComputeCanonicalSamples must have been called first.
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147 const TrainingSample *GetCanonicalSample(int font_id, int class_id) const;
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148 // Gets the max distance for the given canonical sample.
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149 // ComputeCanonicalSamples must have been called first.
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150 float GetCanonicalDist(int font_id, int class_id) const;
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151
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152 // Returns a mutable pointer to the sample with the given index.
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153 TrainingSample *mutable_sample(int index) {
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154 return samples_[index];
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155 }
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156 // Gets ownership of the sample with the given index, removing it from this.
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157 TrainingSample *extract_sample(int index) {
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158 TrainingSample *sample = samples_[index];
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159 samples_[index] = nullptr;
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160 return sample;
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161 }
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162
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163 // Generates indexed features for all samples with the supplied feature_space.
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164 void IndexFeatures(const IntFeatureSpace &feature_space);
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165
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166 // Marks the given sample for deletion.
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167 // Deletion is actually completed by DeleteDeadSamples.
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168 void KillSample(TrainingSample *sample);
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169
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170 // Deletes all samples with a negative sample index marked by KillSample.
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171 // Must be called before OrganizeByFontAndClass, and OrganizeByFontAndClass
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172 // must be called after as the samples have been renumbered.
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173 void DeleteDeadSamples();
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174
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175 // Construct an array to access the samples by font,class pair.
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176 void OrganizeByFontAndClass();
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177
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178 // Constructs the font_id_map_ which maps real font_ids (sparse) to a compact
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179 // index for the font_class_array_.
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180 void SetupFontIdMap();
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181
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182 // Finds the sample for each font, class pair that has least maximum
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183 // distance to all the other samples of the same font, class.
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184 // OrganizeByFontAndClass must have been already called.
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185 void ComputeCanonicalSamples(const IntFeatureMap &map, bool debug);
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186
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187 // Replicates the samples to a minimum frequency defined by
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188 // 2 * kSampleRandomSize, or for larger counts duplicates all samples.
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189 // After replication, the replicated samples are perturbed slightly, but
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190 // in a predictable and repeatable way.
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191 // Use after OrganizeByFontAndClass().
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192 void ReplicateAndRandomizeSamples();
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193
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194 // Caches the indexed features of the canonical samples.
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195 // ComputeCanonicalSamples must have been already called.
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196 void ComputeCanonicalFeatures();
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197 // Computes the combined set of features used by all the samples of each
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198 // font/class combination. Use after ReplicateAndRandomizeSamples.
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199 void ComputeCloudFeatures(int feature_space_size);
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200
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201 // Adds all fonts of the given class to the shape.
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202 void AddAllFontsForClass(int class_id, Shape *shape) const;
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203
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204 // Display the samples with the given indexed feature that also match
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205 // the given shape.
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206 void DisplaySamplesWithFeature(int f_index, const Shape &shape,
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207 const IntFeatureSpace &feature_space, ScrollView::Color color,
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208 ScrollView *window) const;
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209
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210 private:
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211 // Struct to store a triplet of unichar, font, distance in the distance cache.
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212 struct FontClassDistance {
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213 int unichar_id;
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214 int font_id; // Real font id.
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215 float distance;
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216 };
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217 // Simple struct to store information related to each font/class combination.
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218 struct FontClassInfo {
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219 FontClassInfo();
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220
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221 // Writes to the given file. Returns false in case of error.
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222 bool Serialize(FILE *fp) const;
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223 // Reads from the given file. Returns false in case of error.
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224 // If swap is true, assumes a big/little-endian swap is needed.
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225 bool DeSerialize(bool swap, FILE *fp);
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226
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227 // Number of raw samples.
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228 int32_t num_raw_samples;
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229 // Index of the canonical sample.
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230 int32_t canonical_sample;
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231 // Max distance of the canonical sample from any other.
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232 float canonical_dist;
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233 // Sample indices for the samples, including replicated.
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234 std::vector<int32_t> samples;
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235
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236 // Non-serialized cache data.
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237 // Indexed features of the canonical sample.
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238 std::vector<int> canonical_features;
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239 // The mapped features of all the samples.
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240 BitVector cloud_features;
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241
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242 // Caches for ClusterDistance.
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243 // Caches for other fonts but matching this unichar. -1 indicates not set.
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244 // Indexed by compact font index from font_id_map_.
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245 std::vector<float> font_distance_cache;
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246 // Caches for other unichars but matching this font. -1 indicates not set.
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247 std::vector<float> unichar_distance_cache;
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248 // Cache for the rest (non matching font and unichar.)
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249 // A cache of distances computed by ReliablySeparable.
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250 std::vector<FontClassDistance> distance_cache;
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251 };
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252
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253 std::vector<TrainingSample *> samples_;
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254 // Number of samples before replication/randomization.
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255 int num_raw_samples_;
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256 // Character set we are training for.
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257 UNICHARSET unicharset_;
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258 // Character set size to which the 2-d arrays below refer.
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259 int unicharset_size_;
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260 // Map to allow the font_class_array_ below to be compact.
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261 // The sparse space is the real font_id, used in samples_ .
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262 // The compact space is an index to font_class_array_
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263 IndexMapBiDi font_id_map_;
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264 // A 2-d array of FontClassInfo holding information related to each
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265 // (font_id, class_id) pair.
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266 GENERIC_2D_ARRAY<FontClassInfo> *font_class_array_;
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267
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268 // Reference to the fontinfo_table_ in MasterTrainer. Provides names
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269 // for font_ids in the samples. Not serialized!
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270 const FontInfoTable &fontinfo_table_;
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271 };
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272
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273 } // namespace tesseract.
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274
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275 #endif // TRAININGSAMPLESETSET_H_