Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/classify/normmatch.cpp @ 38:8934ac156ef5
Allow to build with the PyPI package "clang" instead of "libclang".
1. It seems to be maintained.
2. In the FreeBSD base system there is no pre-built libclang.so. If you
need this library you have to install llvm from ports additionally.
2. On FreeBSD there is no pre-built wheel "libclang" with a packaged
libclang.so.
| author | Franz Glasner <fzglas.hg@dom66.de> |
|---|---|
| date | Tue, 23 Sep 2025 10:27:15 +0200 |
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| rev | line source |
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1 /****************************************************************************** |
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2 ** Filename: normmatch.c |
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3 ** Purpose: Simple matcher based on character normalization features. |
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4 ** Author: Dan Johnson |
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5 ** |
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6 ** (c) Copyright Hewlett-Packard Company, 1988. |
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7 ** Licensed under the Apache License, Version 2.0 (the "License"); |
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8 ** you may not use this file except in compliance with the License. |
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9 ** You may obtain a copy of the License at |
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10 ** http://www.apache.org/licenses/LICENSE-2.0 |
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11 ** Unless required by applicable law or agreed to in writing, software |
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12 ** distributed under the License is distributed on an "AS IS" BASIS, |
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13 ** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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14 ** See the License for the specific language governing permissions and |
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15 ** limitations under the License. |
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16 ******************************************************************************/ |
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17 /*---------------------------------------------------------------------------- |
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18 Include Files and Type Defines |
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19 ----------------------------------------------------------------------------*/ |
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20 #include "normmatch.h" |
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21 |
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22 #include "classify.h" |
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23 #include "clusttool.h" |
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24 #include "helpers.h" |
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25 #include "normfeat.h" |
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26 #include "params.h" |
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27 #include "unicharset.h" |
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28 |
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29 #include <cmath> |
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30 #include <cstdio> |
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31 #include <sstream> // for std::istringstream |
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32 |
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33 namespace tesseract { |
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34 |
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35 struct NORM_PROTOS { |
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36 NORM_PROTOS(size_t n) : NumProtos(n), Protos(n) { |
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37 } |
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38 int NumParams = 0; |
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39 int NumProtos; |
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40 PARAM_DESC *ParamDesc = nullptr; |
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41 std::vector<LIST> Protos; |
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42 }; |
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43 |
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44 /*---------------------------------------------------------------------------- |
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45 Private Code |
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46 ----------------------------------------------------------------------------*/ |
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47 |
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48 /** |
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49 * @name NormEvidenceOf |
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50 * |
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51 * Return the new type of evidence number corresponding to this |
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52 * normalization adjustment. The equation that represents the transform is: |
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53 * 1 / (1 + (NormAdj / midpoint) ^ curl) |
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54 */ |
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55 static float NormEvidenceOf(float NormAdj) { |
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56 NormAdj /= static_cast<float>(classify_norm_adj_midpoint); |
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57 |
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58 if (classify_norm_adj_curl == 3) { |
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59 NormAdj = NormAdj * NormAdj * NormAdj; |
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60 } else if (classify_norm_adj_curl == 2) { |
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61 NormAdj = NormAdj * NormAdj; |
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62 } else { |
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63 NormAdj = std::pow(NormAdj, static_cast<float>(classify_norm_adj_curl)); |
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64 } |
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65 return (1 / (1 + NormAdj)); |
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66 } |
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67 |
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68 /*---------------------------------------------------------------------------- |
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69 Variables |
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70 ----------------------------------------------------------------------------*/ |
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71 |
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72 /** control knobs used to control the normalization adjustment process */ |
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73 double_VAR(classify_norm_adj_midpoint, 32.0, "Norm adjust midpoint ..."); |
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74 double_VAR(classify_norm_adj_curl, 2.0, "Norm adjust curl ..."); |
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75 /** Weight of width variance against height and vertical position. */ |
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76 const float kWidthErrorWeighting = 0.125f; |
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77 |
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78 /*---------------------------------------------------------------------------- |
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79 Public Code |
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80 ----------------------------------------------------------------------------*/ |
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81 /** |
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82 * This routine compares Features against each character |
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83 * normalization proto for ClassId and returns the match |
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84 * rating of the best match. |
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85 * @param ClassId id of class to match against |
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86 * @param feature character normalization feature |
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87 * @param DebugMatch controls dump of debug info |
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88 * |
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89 * Globals: |
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90 * #NormProtos character normalization prototypes |
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91 * |
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92 * @return Best match rating for Feature against protos of ClassId. |
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93 */ |
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94 float Classify::ComputeNormMatch(CLASS_ID ClassId, const FEATURE_STRUCT &feature, bool DebugMatch) { |
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95 if (ClassId >= NormProtos->NumProtos) { |
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96 ClassId = NO_CLASS; |
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97 } |
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98 |
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99 /* handle requests for classification as noise */ |
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100 if (ClassId == NO_CLASS) { |
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101 /* kludge - clean up constants and make into control knobs later */ |
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102 float Match = (feature.Params[CharNormLength] * feature.Params[CharNormLength] * 500.0f + |
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103 feature.Params[CharNormRx] * feature.Params[CharNormRx] * 8000.0f + |
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104 feature.Params[CharNormRy] * feature.Params[CharNormRy] * 8000.0f); |
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105 return (1 - NormEvidenceOf(Match)); |
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106 } |
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107 |
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108 if (DebugMatch) { |
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109 tprintf("\nChar norm for class %s\n", unicharset.id_to_unichar(ClassId)); |
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110 } |
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111 |
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112 LIST Protos = NormProtos->Protos[ClassId]; |
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113 if (Protos == nullptr) { |
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114 // Avoid FP overflow in NormEvidenceOf. |
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115 return 1.0f; |
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116 } |
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117 |
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118 float BestMatch = FLT_MAX; |
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119 iterate(Protos) { |
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120 auto Proto = reinterpret_cast<PROTOTYPE *>(Protos->first_node()); |
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121 float Delta = feature.Params[CharNormY] - Proto->Mean[CharNormY]; |
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122 float Match = Delta * Delta * Proto->Weight.Elliptical[CharNormY]; |
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123 if (DebugMatch) { |
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124 tprintf("YMiddle: Proto=%g, Delta=%g, Var=%g, Dist=%g\n", Proto->Mean[CharNormY], Delta, |
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125 Proto->Weight.Elliptical[CharNormY], Match); |
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126 } |
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127 Delta = feature.Params[CharNormRx] - Proto->Mean[CharNormRx]; |
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128 Match += Delta * Delta * Proto->Weight.Elliptical[CharNormRx]; |
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129 if (DebugMatch) { |
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130 tprintf("Height: Proto=%g, Delta=%g, Var=%g, Dist=%g\n", Proto->Mean[CharNormRx], Delta, |
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131 Proto->Weight.Elliptical[CharNormRx], Match); |
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132 } |
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133 // Ry is width! See intfx.cpp. |
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134 Delta = feature.Params[CharNormRy] - Proto->Mean[CharNormRy]; |
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135 if (DebugMatch) { |
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136 tprintf("Width: Proto=%g, Delta=%g, Var=%g\n", Proto->Mean[CharNormRy], Delta, |
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137 Proto->Weight.Elliptical[CharNormRy]); |
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138 } |
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139 Delta = Delta * Delta * Proto->Weight.Elliptical[CharNormRy]; |
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140 Delta *= kWidthErrorWeighting; |
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141 Match += Delta; |
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142 if (DebugMatch) { |
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143 tprintf("Total Dist=%g, scaled=%g, sigmoid=%g, penalty=%g\n", Match, |
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144 Match / classify_norm_adj_midpoint, NormEvidenceOf(Match), |
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145 256 * (1 - NormEvidenceOf(Match))); |
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146 } |
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147 |
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148 if (Match < BestMatch) { |
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149 BestMatch = Match; |
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150 } |
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151 } |
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152 return 1 - NormEvidenceOf(BestMatch); |
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153 } /* ComputeNormMatch */ |
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154 |
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155 void Classify::FreeNormProtos() { |
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156 if (NormProtos != nullptr) { |
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157 for (int i = 0; i < NormProtos->NumProtos; i++) { |
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158 FreeProtoList(&NormProtos->Protos[i]); |
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159 } |
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160 delete[] NormProtos->ParamDesc; |
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161 delete NormProtos; |
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162 NormProtos = nullptr; |
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163 } |
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164 } |
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165 |
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166 /** |
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167 * This routine allocates a new data structure to hold |
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168 * a set of character normalization protos. It then fills in |
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169 * the data structure by reading from the specified File. |
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170 * @param fp open text file to read normalization protos from |
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171 * Globals: none |
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172 * @return Character normalization protos. |
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173 */ |
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174 NORM_PROTOS *Classify::ReadNormProtos(TFile *fp) { |
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175 char unichar[2 * UNICHAR_LEN + 1]; |
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176 UNICHAR_ID unichar_id; |
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177 LIST Protos; |
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178 int NumProtos; |
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179 |
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180 /* allocate and initialization data structure */ |
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181 auto NormProtos = new NORM_PROTOS(unicharset.size()); |
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182 |
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183 /* read file header and save in data structure */ |
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184 NormProtos->NumParams = ReadSampleSize(fp); |
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185 NormProtos->ParamDesc = ReadParamDesc(fp, NormProtos->NumParams); |
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186 |
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187 /* read protos for each class into a separate list */ |
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188 const int kMaxLineSize = 100; |
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189 char line[kMaxLineSize]; |
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190 while (fp->FGets(line, kMaxLineSize) != nullptr) { |
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191 std::istringstream stream(line); |
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192 stream.imbue(std::locale::classic()); |
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193 stream >> unichar >> NumProtos; |
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194 if (stream.fail()) { |
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195 continue; |
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196 } |
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197 if (unicharset.contains_unichar(unichar)) { |
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198 unichar_id = unicharset.unichar_to_id(unichar); |
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199 Protos = NormProtos->Protos[unichar_id]; |
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200 for (int i = 0; i < NumProtos; i++) { |
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201 Protos = push_last(Protos, ReadPrototype(fp, NormProtos->NumParams)); |
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202 } |
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203 NormProtos->Protos[unichar_id] = Protos; |
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204 } else { |
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205 tprintf("Error: unichar %s in normproto file is not in unichar set.\n", unichar); |
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206 for (int i = 0; i < NumProtos; i++) { |
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207 FreePrototype(ReadPrototype(fp, NormProtos->NumParams)); |
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208 } |
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209 } |
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210 } |
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211 return NormProtos; |
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212 } /* ReadNormProtos */ |
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213 |
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214 } // namespace tesseract |
