Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/training/common/sampleiterator.cpp @ 2:b50eed0cc0ef upstream
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| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Mon, 15 Sep 2025 11:43:07 +0200 |
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1 // Copyright 2011 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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15 |
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16 #include "sampleiterator.h" |
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17 |
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18 #include "intfeaturemap.h" |
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19 |
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20 #include "indexmapbidi.h" |
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21 #include "shapetable.h" |
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22 #include "trainingsample.h" |
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23 #include "trainingsampleset.h" |
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24 |
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25 namespace tesseract { |
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26 |
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27 // ================== SampleIterator Implementation ================= |
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28 |
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29 SampleIterator::SampleIterator() |
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30 : charset_map_(nullptr) |
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31 , shape_table_(nullptr) |
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32 , sample_set_(nullptr) |
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33 , randomize_(false) |
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34 , owned_shape_table_(nullptr) { |
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35 num_shapes_ = 0; |
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36 Begin(); |
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37 } |
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38 |
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39 SampleIterator::~SampleIterator() { |
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40 Clear(); |
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41 } |
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42 |
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43 void SampleIterator::Clear() { |
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44 delete owned_shape_table_; |
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45 owned_shape_table_ = nullptr; |
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46 } |
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47 |
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48 // See class comment for arguments. |
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49 void SampleIterator::Init(const IndexMapBiDi *charset_map, const ShapeTable *shape_table, |
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50 bool randomize, TrainingSampleSet *sample_set) { |
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51 Clear(); |
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52 charset_map_ = charset_map; |
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53 shape_table_ = shape_table; |
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54 sample_set_ = sample_set; |
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55 randomize_ = randomize; |
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56 if (shape_table_ == nullptr && charset_map_ != nullptr) { |
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57 // The caller wishes to iterate by class. The easiest way to do this |
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58 // is to create a dummy shape_table_ that we will own. |
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59 int num_fonts = sample_set_->NumFonts(); |
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60 owned_shape_table_ = new ShapeTable(sample_set_->unicharset()); |
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61 int charsetsize = sample_set_->unicharset().size(); |
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62 for (int c = 0; c < charsetsize; ++c) { |
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63 // We always add a shape for each character to keep the index in sync |
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64 // with the unichar_id. |
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65 int shape_id = owned_shape_table_->AddShape(c, 0); |
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66 for (int f = 1; f < num_fonts; ++f) { |
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67 if (sample_set_->NumClassSamples(f, c, true) > 0) { |
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68 owned_shape_table_->AddToShape(shape_id, c, f); |
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69 } |
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70 } |
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71 } |
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72 shape_table_ = owned_shape_table_; |
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73 } |
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74 if (shape_table_ != nullptr) { |
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75 num_shapes_ = shape_table_->NumShapes(); |
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76 } else { |
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77 num_shapes_ = randomize ? sample_set_->num_samples() : sample_set_->num_raw_samples(); |
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78 } |
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79 Begin(); |
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80 } |
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81 |
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82 // Iterator functions designed for use with a simple for loop: |
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83 // for (it.Begin(); !it.AtEnd(); it.Next()) { |
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84 // const TrainingSample& sample = it.GetSample(); |
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85 // } |
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86 void SampleIterator::Begin() { |
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87 shape_index_ = -1; |
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|
88 shape_char_index_ = 0; |
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|
89 num_shape_chars_ = 0; |
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|
90 shape_font_index_ = 0; |
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|
91 num_shape_fonts_ = 0; |
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|
92 sample_index_ = 0; |
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|
93 num_samples_ = 0; |
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|
94 // Find the first indexable sample. |
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|
95 Next(); |
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96 } |
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97 |
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|
98 bool SampleIterator::AtEnd() const { |
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|
99 return shape_index_ >= num_shapes_; |
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100 } |
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101 |
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|
102 const TrainingSample &SampleIterator::GetSample() const { |
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103 if (shape_table_ != nullptr) { |
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104 const UnicharAndFonts *shape_entry = GetShapeEntry(); |
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105 int char_id = shape_entry->unichar_id; |
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106 int font_id = shape_entry->font_ids[shape_font_index_]; |
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107 return *sample_set_->GetSample(font_id, char_id, sample_index_); |
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108 } else { |
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109 return *sample_set_->GetSample(shape_index_); |
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110 } |
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111 } |
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112 |
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|
113 TrainingSample *SampleIterator::MutableSample() const { |
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114 if (shape_table_ != nullptr) { |
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115 const UnicharAndFonts *shape_entry = GetShapeEntry(); |
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116 int char_id = shape_entry->unichar_id; |
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117 int font_id = shape_entry->font_ids[shape_font_index_]; |
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118 return sample_set_->MutableSample(font_id, char_id, sample_index_); |
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119 } else { |
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120 return sample_set_->mutable_sample(shape_index_); |
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121 } |
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122 } |
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123 |
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124 // Returns the total index (from the original set of samples) of the current |
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125 // sample. |
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|
126 int SampleIterator::GlobalSampleIndex() const { |
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127 if (shape_table_ != nullptr) { |
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128 const UnicharAndFonts *shape_entry = GetShapeEntry(); |
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129 int char_id = shape_entry->unichar_id; |
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130 int font_id = shape_entry->font_ids[shape_font_index_]; |
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131 return sample_set_->GlobalSampleIndex(font_id, char_id, sample_index_); |
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132 } else { |
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133 return shape_index_; |
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134 } |
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135 } |
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136 |
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137 // Returns the index of the current sample in compact charset space, so |
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138 // in a 2-class problem between x and y, the returned indices will all be |
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139 // 0 or 1, and have nothing to do with the unichar_ids. |
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140 // If the charset_map_ is nullptr, then this is equal to GetSparseClassID(). |
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|
141 int SampleIterator::GetCompactClassID() const { |
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142 return charset_map_ != nullptr ? charset_map_->SparseToCompact(shape_index_) : GetSparseClassID(); |
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143 } |
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144 // Returns the index of the current sample in sparse charset space, so |
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|
145 // in a 2-class problem between x and y, the returned indices will all be |
|
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|
146 // x or y, where x and y may be unichar_ids (no shape_table_) or shape_ids |
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147 // with a shape_table_. |
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|
148 int SampleIterator::GetSparseClassID() const { |
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149 return shape_table_ != nullptr ? shape_index_ : GetSample().class_id(); |
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150 } |
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151 |
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152 // Moves on to the next indexable sample. If the end is reached, leaves |
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153 // the state such that AtEnd() is true. |
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|
154 void SampleIterator::Next() { |
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|
155 if (shape_table_ != nullptr) { |
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|
156 // Next sample in this class/font combination. |
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|
157 ++sample_index_; |
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|
158 if (sample_index_ < num_samples_) { |
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159 return; |
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160 } |
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|
161 // Next font in this class in this shape. |
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|
162 sample_index_ = 0; |
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163 do { |
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|
164 ++shape_font_index_; |
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|
165 if (shape_font_index_ >= num_shape_fonts_) { |
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166 // Next unichar in this shape. |
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|
167 shape_font_index_ = 0; |
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|
168 ++shape_char_index_; |
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|
169 if (shape_char_index_ >= num_shape_chars_) { |
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170 // Find the next shape that is mapped in the charset_map_. |
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|
171 shape_char_index_ = 0; |
|
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|
172 do { |
|
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|
173 ++shape_index_; |
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174 } while (shape_index_ < num_shapes_ && charset_map_ != nullptr && |
|
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|
175 charset_map_->SparseToCompact(shape_index_) < 0); |
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176 if (shape_index_ >= num_shapes_) { |
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177 return; // The end. |
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178 } |
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179 num_shape_chars_ = shape_table_->GetShape(shape_index_).size(); |
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180 } |
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181 } |
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182 const UnicharAndFonts *shape_entry = GetShapeEntry(); |
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183 num_shape_fonts_ = shape_entry->font_ids.size(); |
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184 int char_id = shape_entry->unichar_id; |
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185 int font_id = shape_entry->font_ids[shape_font_index_]; |
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186 num_samples_ = sample_set_->NumClassSamples(font_id, char_id, randomize_); |
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187 } while (num_samples_ == 0); |
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188 } else { |
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189 // We are just iterating over the samples. |
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190 ++shape_index_; |
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191 } |
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192 } |
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193 |
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194 // Returns the size of the compact charset space. |
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195 int SampleIterator::CompactCharsetSize() const { |
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196 return charset_map_ != nullptr ? charset_map_->CompactSize() : SparseCharsetSize(); |
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197 } |
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198 |
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199 // Returns the size of the sparse charset space. |
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200 int SampleIterator::SparseCharsetSize() const { |
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201 return charset_map_ != nullptr |
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202 ? charset_map_->SparseSize() |
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203 : (shape_table_ != nullptr ? shape_table_->NumShapes() : sample_set_->charsetsize()); |
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204 } |
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205 |
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206 // Sets the mapped_features_ from the features using the provided |
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207 // feature_map. |
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208 static void MapFeatures(TrainingSample &s, const IntFeatureMap &feature_map) { |
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209 std::vector<int> indexed_features; |
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210 feature_map.feature_space().IndexAndSortFeatures(s.features(), s.num_features(), |
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211 &indexed_features); |
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212 feature_map.MapIndexedFeatures(indexed_features, &s.mapped_features_); |
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213 s.features_are_indexed_ = false; |
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214 s.features_are_mapped_ = true; |
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215 } |
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216 |
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217 // Apply the supplied feature_space/feature_map transform to all samples |
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218 // accessed by this iterator. |
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219 void SampleIterator::MapSampleFeatures(const IntFeatureMap &feature_map) { |
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220 for (Begin(); !AtEnd(); Next()) { |
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221 TrainingSample *sample = MutableSample(); |
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222 MapFeatures(*sample, feature_map); |
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223 } |
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224 } |
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225 |
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226 // Adjust the weights of all the samples to be uniform in the given charset. |
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227 // Returns the number of samples in the iterator. |
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228 int SampleIterator::UniformSamples() { |
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229 int num_good_samples = 0; |
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230 for (Begin(); !AtEnd(); Next()) { |
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231 TrainingSample *sample = MutableSample(); |
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232 sample->set_weight(1.0); |
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233 ++num_good_samples; |
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234 } |
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235 NormalizeSamples(); |
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236 return num_good_samples; |
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237 } |
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238 |
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239 // Normalize the weights of all the samples in the charset_map so they sum |
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240 // to 1. Returns the minimum assigned sample weight. |
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241 double SampleIterator::NormalizeSamples() { |
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242 double total_weight = 0.0; |
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243 for (Begin(); !AtEnd(); Next()) { |
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244 const TrainingSample &sample = GetSample(); |
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245 total_weight += sample.weight(); |
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246 } |
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247 // Normalize samples. |
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248 double min_assigned_sample_weight = 1.0; |
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249 if (total_weight > 0.0) { |
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250 for (Begin(); !AtEnd(); Next()) { |
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251 TrainingSample *sample = MutableSample(); |
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252 double weight = sample->weight() / total_weight; |
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253 if (weight < min_assigned_sample_weight) { |
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254 min_assigned_sample_weight = weight; |
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255 } |
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256 sample->set_weight(weight); |
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257 } |
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258 } |
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259 return min_assigned_sample_weight; |
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260 } |
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261 |
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262 // Helper returns the current UnicharAndFont shape_entry. |
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263 const UnicharAndFonts *SampleIterator::GetShapeEntry() const { |
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264 const Shape &shape = shape_table_->GetShape(shape_index_); |
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265 return &shape[shape_char_index_]; |
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266 } |
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267 |
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parents:
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268 } // namespace tesseract. |
