Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/wordrec/lm_pain_points.cpp @ 40:aa33339d6b8a upstream
ADD: MuPDF v1.26.10: the MuPDF source as downloaded by a default build of PyMuPDF 1.26.5.
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 11:31:38 +0200 |
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| rev | line source |
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1 /////////////////////////////////////////////////////////////////////// |
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2 // File: pain_points.cpp |
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3 // Description: Functions that utilize the knowledge about the properties |
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4 // of the paths explored by the segmentation search in order |
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5 // to "pain points" - the locations in the ratings matrix |
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6 // which should be classified next. |
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7 // Author: Rika Antonova |
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8 // Created: Mon Jun 20 11:26:43 PST 2012 |
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9 // |
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10 // (C) Copyright 2012, Google Inc. |
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11 // Licensed under the Apache License, Version 2.0 (the "License"); |
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12 // you may not use this file except in compliance with the License. |
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13 // You may obtain a copy of the License at |
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14 // http://www.apache.org/licenses/LICENSE-2.0 |
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15 // Unless required by applicable law or agreed to in writing, software |
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16 // distributed under the License is distributed on an "AS IS" BASIS, |
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17 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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18 // See the License for the specific language governing permissions and |
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19 // limitations under the License. |
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20 // |
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21 /////////////////////////////////////////////////////////////////////// |
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22 |
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23 #include "lm_pain_points.h" |
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24 |
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25 #include "associate.h" |
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26 #include "dict.h" |
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27 #include "genericheap.h" |
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28 #include "lm_state.h" |
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29 #include "matrix.h" |
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30 #include "pageres.h" |
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31 |
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32 #include <algorithm> |
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33 |
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34 namespace tesseract { |
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35 |
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36 const float LMPainPoints::kDefaultPainPointPriorityAdjustment = 2.0f; |
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37 const float LMPainPoints::kLooseMaxCharWhRatio = 2.5f; |
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38 |
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39 LMPainPointsType LMPainPoints::Deque(MATRIX_COORD *pp, float *priority) { |
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40 for (int h = 0; h < LM_PPTYPE_NUM; ++h) { |
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41 if (pain_points_heaps_[h].empty()) { |
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42 continue; |
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43 } |
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44 *priority = pain_points_heaps_[h].PeekTop().key(); |
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45 *pp = pain_points_heaps_[h].PeekTop().data(); |
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46 pain_points_heaps_[h].Pop(nullptr); |
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47 return static_cast<LMPainPointsType>(h); |
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48 } |
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49 return LM_PPTYPE_NUM; |
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50 } |
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51 |
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52 void LMPainPoints::GenerateInitial(WERD_RES *word_res) { |
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53 MATRIX *ratings = word_res->ratings; |
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54 AssociateStats associate_stats; |
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55 for (int col = 0; col < ratings->dimension(); ++col) { |
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56 int row_end = std::min(ratings->dimension(), col + ratings->bandwidth() + 1); |
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57 for (int row = col + 1; row < row_end; ++row) { |
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58 MATRIX_COORD coord(col, row); |
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59 if (coord.Valid(*ratings) && ratings->get(col, row) != NOT_CLASSIFIED) { |
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60 continue; |
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61 } |
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62 // Add an initial pain point if needed. |
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63 if (ratings->Classified(col, row - 1, dict_->WildcardID()) || |
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64 (col + 1 < ratings->dimension() && |
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65 ratings->Classified(col + 1, row, dict_->WildcardID()))) { |
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66 GeneratePainPoint(col, row, LM_PPTYPE_SHAPE, 0.0, true, max_char_wh_ratio_, word_res); |
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67 } |
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68 } |
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69 } |
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70 } |
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71 |
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72 void LMPainPoints::GenerateFromPath(float rating_cert_scale, ViterbiStateEntry *vse, |
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73 WERD_RES *word_res) { |
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74 ViterbiStateEntry *curr_vse = vse; |
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75 BLOB_CHOICE *curr_b = vse->curr_b; |
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76 // The following pain point generation and priority calculation approaches |
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77 // prioritize exploring paths with low average rating of the known part of |
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78 // the path, while not relying on the ratings of the pieces to be combined. |
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79 // |
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80 // A pain point to combine the neighbors is generated for each pair of |
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81 // neighboring blobs on the path (the path is represented by vse argument |
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82 // given to GenerateFromPath()). The priority of each pain point is set to |
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83 // the average rating (per outline length) of the path, not including the |
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84 // ratings of the blobs to be combined. |
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85 // The ratings of the blobs to be combined are not used to calculate the |
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86 // priority, since it is not possible to determine from their magnitude |
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87 // whether it will be beneficial to combine the blobs. The reason is that |
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88 // chopped junk blobs (/ | - ') can have very good (low) ratings, however |
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89 // combining them will be beneficial. Blobs with high ratings might be |
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90 // over-joined pieces of characters, but also could be blobs from an unseen |
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91 // font or chopped pieces of complex characters. |
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92 while (curr_vse->parent_vse != nullptr) { |
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93 ViterbiStateEntry *parent_vse = curr_vse->parent_vse; |
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94 const MATRIX_COORD &curr_cell = curr_b->matrix_cell(); |
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95 const MATRIX_COORD &parent_cell = parent_vse->curr_b->matrix_cell(); |
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96 MATRIX_COORD pain_coord(parent_cell.col, curr_cell.row); |
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97 if (!pain_coord.Valid(*word_res->ratings) || |
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98 !word_res->ratings->Classified(parent_cell.col, curr_cell.row, dict_->WildcardID())) { |
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99 // rat_subtr contains ratings sum of the two adjacent blobs to be merged. |
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100 // rat_subtr will be subtracted from the ratings sum of the path, since |
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101 // the blobs will be joined into a new blob, whose rating is yet unknown. |
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102 float rat_subtr = curr_b->rating() + parent_vse->curr_b->rating(); |
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103 // ol_subtr contains the outline length of the blobs that will be joined. |
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104 float ol_subtr = |
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105 AssociateUtils::ComputeOutlineLength(rating_cert_scale, *curr_b) + |
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106 AssociateUtils::ComputeOutlineLength(rating_cert_scale, *(parent_vse->curr_b)); |
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107 // ol_dif is the outline of the path without the two blobs to be joined. |
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108 float ol_dif = vse->outline_length - ol_subtr; |
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109 // priority is set to the average rating of the path per unit of outline, |
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110 // not counting the ratings of the pieces to be joined. |
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111 float priority = ol_dif > 0 ? (vse->ratings_sum - rat_subtr) / ol_dif : 0.0; |
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112 GeneratePainPoint(pain_coord.col, pain_coord.row, LM_PPTYPE_PATH, priority, true, |
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113 max_char_wh_ratio_, word_res); |
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|
114 } else if (debug_level_ > 3) { |
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115 tprintf("NO pain point (Classified) for col=%d row=%d type=%s\n", pain_coord.col, |
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116 pain_coord.row, LMPainPointsTypeName[LM_PPTYPE_PATH]); |
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117 BLOB_CHOICE_IT b_it(word_res->ratings->get(pain_coord.col, pain_coord.row)); |
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118 for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) { |
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119 BLOB_CHOICE *choice = b_it.data(); |
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120 choice->print_full(); |
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121 } |
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122 } |
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123 |
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124 curr_vse = parent_vse; |
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125 curr_b = curr_vse->curr_b; |
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126 } |
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127 } |
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128 |
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129 void LMPainPoints::GenerateFromAmbigs(const DANGERR &fixpt, ViterbiStateEntry *vse, |
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130 WERD_RES *word_res) { |
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131 // Begins and ends in DANGERR vector now record the blob indices as used |
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132 // by the ratings matrix. |
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133 for (auto &&danger : fixpt) { |
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134 // Only use dangerous ambiguities. |
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135 if (danger.dangerous) { |
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|
136 GeneratePainPoint(danger.begin, danger.end - 1, LM_PPTYPE_AMBIG, vse->cost, true, |
|
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|
137 kLooseMaxCharWhRatio, word_res); |
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138 } |
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139 } |
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140 } |
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141 |
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|
142 bool LMPainPoints::GeneratePainPoint(int col, int row, LMPainPointsType pp_type, |
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|
143 float special_priority, bool ok_to_extend, |
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|
144 float max_char_wh_ratio, WERD_RES *word_res) { |
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|
145 MATRIX_COORD coord(col, row); |
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146 if (coord.Valid(*word_res->ratings) && |
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147 word_res->ratings->Classified(col, row, dict_->WildcardID())) { |
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|
148 return false; |
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149 } |
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|
150 if (debug_level_ > 3) { |
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|
151 tprintf("Generating pain point for col=%d row=%d type=%s\n", col, row, |
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|
152 LMPainPointsTypeName[pp_type]); |
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153 } |
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|
154 // Compute associate stats. |
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|
155 AssociateStats associate_stats; |
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|
156 AssociateUtils::ComputeStats(col, row, nullptr, 0, fixed_pitch_, max_char_wh_ratio, word_res, |
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|
157 debug_level_, &associate_stats); |
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158 // For fixed-pitch fonts/languages: if the current combined blob overlaps |
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159 // the next blob on the right and it is ok to extend the blob, try extending |
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parents:
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|
160 // the blob until there is no overlap with the next blob on the right or |
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|
161 // until the width-to-height ratio becomes too large. |
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|
162 if (ok_to_extend) { |
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parents:
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|
163 while (associate_stats.bad_fixed_pitch_right_gap && row + 1 < word_res->ratings->dimension() && |
|
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parents:
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|
164 !associate_stats.bad_fixed_pitch_wh_ratio) { |
|
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parents:
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|
165 AssociateUtils::ComputeStats(col, ++row, nullptr, 0, fixed_pitch_, max_char_wh_ratio, |
|
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parents:
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|
166 word_res, debug_level_, &associate_stats); |
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167 } |
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168 } |
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parents:
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|
169 if (associate_stats.bad_shape) { |
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170 if (debug_level_ > 3) { |
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171 tprintf("Discarded pain point with a bad shape\n"); |
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172 } |
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173 return false; |
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174 } |
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175 |
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176 // Insert the new pain point into pain_points_heap_. |
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177 if (pain_points_heaps_[pp_type].size() < max_heap_size_) { |
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178 // Compute pain point priority. |
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179 float priority; |
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180 if (pp_type == LM_PPTYPE_PATH) { |
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181 priority = special_priority; |
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182 } else { |
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183 priority = associate_stats.gap_sum; |
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184 } |
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185 MatrixCoordPair pain_point(priority, MATRIX_COORD(col, row)); |
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186 pain_points_heaps_[pp_type].Push(&pain_point); |
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187 if (debug_level_) { |
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188 tprintf("Added pain point with priority %g\n", priority); |
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189 } |
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190 return true; |
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191 } else { |
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192 if (debug_level_) { |
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193 tprintf("Pain points heap is full\n"); |
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194 } |
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195 return false; |
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196 } |
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197 } |
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198 |
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199 /** |
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200 * Adjusts the pain point coordinates to cope with expansion of the ratings |
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201 * matrix due to a split of the blob with the given index. |
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202 */ |
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203 void LMPainPoints::RemapForSplit(int index) { |
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204 for (auto &pain_points_heap : pain_points_heaps_) { |
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205 std::vector<MatrixCoordPair> &heap = pain_points_heap.heap(); |
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206 for (auto &&entry : heap) { |
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207 entry.data().MapForSplit(index); |
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208 } |
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209 } |
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210 } |
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211 |
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212 } // namespace tesseract |
