annotate mupdf-source/thirdparty/tesseract/src/classify/picofeat.cpp @ 10:76e05e28f6a8

Also use the LLVM linker by symlinking and use LLVM's ar now
author Franz Glasner <fzglas.hg@dom66.de>
date Wed, 17 Sep 2025 06:30:46 +0200
parents b50eed0cc0ef
children
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1 /******************************************************************************
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2 ** Filename: picofeat.c
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3 ** Purpose: Definition of pico-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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18 #include "picofeat.h"
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20 #include "classify.h"
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21 #include "featdefs.h"
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22 #include "fpoint.h"
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23 #include "mfoutline.h"
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24 #include "ocrfeatures.h"
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25 #include "params.h"
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26 #include "trainingsample.h"
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28 #include <cmath>
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29 #include <cstdio>
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31 namespace tesseract {
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33 /*---------------------------------------------------------------------------
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34 Variables
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35 ----------------------------------------------------------------------------*/
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37 double_VAR(classify_pico_feature_length, 0.05, "Pico Feature Length");
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39 /*---------------------------------------------------------------------------
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40 Private Function Prototypes
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41 ----------------------------------------------------------------------------*/
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42 void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet);
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44 void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet);
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46 void NormalizePicoX(FEATURE_SET FeatureSet);
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48 /*----------------------------------------------------------------------------
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49 Public Code
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50 ----------------------------------------------------------------------------*/
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51 /*---------------------------------------------------------------------------*/
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52 /**
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53 * Operation: Dummy for now.
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54 *
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55 * Globals:
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56 * - classify_norm_method normalization method currently specified
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57 * @param Blob blob to extract pico-features from
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58 * @return Pico-features for Blob.
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59 */
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60 FEATURE_SET Classify::ExtractPicoFeatures(TBLOB *Blob) {
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61 auto FeatureSet = new FEATURE_SET_STRUCT(MAX_PICO_FEATURES);
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62 auto Outlines = ConvertBlob(Blob);
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63 float XScale, YScale;
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64 NormalizeOutlines(Outlines, &XScale, &YScale);
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65 auto RemainingOutlines = Outlines;
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66 iterate(RemainingOutlines) {
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67 auto Outline = static_cast<MFOUTLINE>(RemainingOutlines->first_node());
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68 ConvertToPicoFeatures2(Outline, FeatureSet);
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69 }
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70 if (classify_norm_method == baseline) {
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71 NormalizePicoX(FeatureSet);
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72 }
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73 FreeOutlines(Outlines);
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74 return (FeatureSet);
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76 } /* ExtractPicoFeatures */
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78 /*----------------------------------------------------------------------------
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79 Private Code
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80 ----------------------------------------------------------------------------*/
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81 /*---------------------------------------------------------------------------*/
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82 /**
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83 * This routine converts an entire segment of an outline
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84 * into a set of pico features which are added to
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85 * FeatureSet. The length of the segment is rounded to the
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86 * nearest whole number of pico-features. The pico-features
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87 * are spaced evenly over the entire segment.
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88 * Results are placed in FeatureSet.
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89 * Globals:
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90 * - classify_pico_feature_length length of a single pico-feature
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91 * @param Start starting point of pico-feature
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92 * @param End ending point of pico-feature
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93 * @param FeatureSet set to add pico-feature to
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94 */
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95 void ConvertSegmentToPicoFeat(FPOINT *Start, FPOINT *End, FEATURE_SET FeatureSet) {
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96 float Angle;
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97 float Length;
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98 int NumFeatures;
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99 FPOINT Center;
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100 FPOINT Delta;
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101 int i;
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102
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103 Angle = NormalizedAngleFrom(Start, End, 1.0);
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104 Length = DistanceBetween(*Start, *End);
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105 NumFeatures = static_cast<int>(floor(Length / classify_pico_feature_length + 0.5));
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parents:
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106 if (NumFeatures < 1) {
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107 NumFeatures = 1;
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108 }
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109
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110 /* compute vector for one pico feature */
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Franz Glasner <fzglas.hg@dom66.de>
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111 Delta.x = XDelta(*Start, *End) / NumFeatures;
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112 Delta.y = YDelta(*Start, *End) / NumFeatures;
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113
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114 /* compute position of first pico feature */
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115 Center.x = Start->x + Delta.x / 2.0;
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116 Center.y = Start->y + Delta.y / 2.0;
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117
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118 /* compute each pico feature in segment and add to feature set */
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119 for (i = 0; i < NumFeatures; i++) {
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120 auto Feature = new FEATURE_STRUCT(&PicoFeatDesc);
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121 Feature->Params[PicoFeatDir] = Angle;
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122 Feature->Params[PicoFeatX] = Center.x;
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123 Feature->Params[PicoFeatY] = Center.y;
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124 AddFeature(FeatureSet, Feature);
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125
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126 Center.x += Delta.x;
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127 Center.y += Delta.y;
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128 }
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129 } /* ConvertSegmentToPicoFeat */
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130
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131 /*---------------------------------------------------------------------------*/
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Franz Glasner <fzglas.hg@dom66.de>
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132 /**
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133 * This routine steps through the specified outline and cuts it
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134 * up into pieces of equal length. These pieces become the
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135 * desired pico-features. Each segment in the outline
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136 * is converted into an integral number of pico-features.
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137 * Results are returned in FeatureSet.
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Franz Glasner <fzglas.hg@dom66.de>
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138 *
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139 * Globals:
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140 * - classify_pico_feature_length length of features to be extracted
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141 * @param Outline outline to extract micro-features from
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142 * @param FeatureSet set of features to add pico-features to
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parents:
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143 */
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144 void ConvertToPicoFeatures2(MFOUTLINE Outline, FEATURE_SET FeatureSet) {
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145 MFOUTLINE Next;
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146 MFOUTLINE First;
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147 MFOUTLINE Current;
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148
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149 if (DegenerateOutline(Outline)) {
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150 return;
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151 }
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152
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153 First = Outline;
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154 Current = First;
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155 Next = NextPointAfter(Current);
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diff changeset
156 do {
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parents:
diff changeset
157 /* note that an edge is hidden if the ending point of the edge is
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parents:
diff changeset
158 marked as hidden. This situation happens because the order of
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parents:
diff changeset
159 the outlines is reversed when they are converted from the old
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parents:
diff changeset
160 format. In the old format, a hidden edge is marked by the
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parents:
diff changeset
161 starting point for that edge. */
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parents:
diff changeset
162 if (!(PointAt(Next)->Hidden)) {
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parents:
diff changeset
163 ConvertSegmentToPicoFeat(&(PointAt(Current)->Point), &(PointAt(Next)->Point), FeatureSet);
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parents:
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164 }
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parents:
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165
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diff changeset
166 Current = Next;
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parents:
diff changeset
167 Next = NextPointAfter(Current);
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parents:
diff changeset
168 } while (Current != First);
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parents:
diff changeset
169
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parents:
diff changeset
170 } /* ConvertToPicoFeatures2 */
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parents:
diff changeset
171
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parents:
diff changeset
172 /*---------------------------------------------------------------------------*/
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
173 /**
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parents:
diff changeset
174 * This routine computes the average x position over all
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parents:
diff changeset
175 * of the pico-features in FeatureSet and then renormalizes
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parents:
diff changeset
176 * the pico-features to force this average to be the x origin
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parents:
diff changeset
177 * (i.e. x=0).
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parents:
diff changeset
178 * FeatureSet is changed.
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parents:
diff changeset
179 * @param FeatureSet pico-features to be normalized
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parents:
diff changeset
180 */
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
181 void NormalizePicoX(FEATURE_SET FeatureSet) {
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parents:
diff changeset
182 int i;
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parents:
diff changeset
183 FEATURE Feature;
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
184 float Origin = 0.0;
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parents:
diff changeset
185
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parents:
diff changeset
186 for (i = 0; i < FeatureSet->NumFeatures; i++) {
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parents:
diff changeset
187 Feature = FeatureSet->Features[i];
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parents:
diff changeset
188 Origin += Feature->Params[PicoFeatX];
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parents:
diff changeset
189 }
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
190 Origin /= FeatureSet->NumFeatures;
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parents:
diff changeset
191
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parents:
diff changeset
192 for (i = 0; i < FeatureSet->NumFeatures; i++) {
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parents:
diff changeset
193 Feature = FeatureSet->Features[i];
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
194 Feature->Params[PicoFeatX] -= Origin;
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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195 }
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
196 } /* NormalizePicoX */
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parents:
diff changeset
197
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parents:
diff changeset
198 /*---------------------------------------------------------------------------*/
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
199 /**
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
200 * @param blob blob to extract features from
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
201 * @param fx_info
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Franz Glasner <fzglas.hg@dom66.de>
parents:
diff changeset
202 * @return Integer character-normalized features for blob.
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Franz Glasner <fzglas.hg@dom66.de>
parents:
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203 */
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204 FEATURE_SET Classify::ExtractIntCNFeatures(const TBLOB &blob, const INT_FX_RESULT_STRUCT &fx_info) {
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205 INT_FX_RESULT_STRUCT local_fx_info(fx_info);
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206 std::vector<INT_FEATURE_STRUCT> bl_features;
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207 tesseract::TrainingSample *sample =
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208 tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
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209 if (sample == nullptr) {
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210 return nullptr;
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211 }
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212
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213 uint32_t num_features = sample->num_features();
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214 const INT_FEATURE_STRUCT *features = sample->features();
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215 auto feature_set = new FEATURE_SET_STRUCT(num_features);
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216 for (uint32_t f = 0; f < num_features; ++f) {
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217 auto feature = new FEATURE_STRUCT(&IntFeatDesc);
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218 feature->Params[IntX] = features[f].X;
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219 feature->Params[IntY] = features[f].Y;
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220 feature->Params[IntDir] = features[f].Theta;
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221 AddFeature(feature_set, feature);
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222 }
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223 delete sample;
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224
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225 return feature_set;
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226 } /* ExtractIntCNFeatures */
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227
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228 /*---------------------------------------------------------------------------*/
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229 /**
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230 * @param blob blob to extract features from
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231 * @param fx_info
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232 * @return Geometric (top/bottom/width) features for blob.
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233 */
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234 FEATURE_SET Classify::ExtractIntGeoFeatures(const TBLOB &blob,
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235 const INT_FX_RESULT_STRUCT &fx_info) {
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236 INT_FX_RESULT_STRUCT local_fx_info(fx_info);
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237 std::vector<INT_FEATURE_STRUCT> bl_features;
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238 tesseract::TrainingSample *sample =
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239 tesseract::BlobToTrainingSample(blob, false, &local_fx_info, &bl_features);
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240 if (sample == nullptr) {
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241 return nullptr;
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242 }
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243
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244 auto feature_set = new FEATURE_SET_STRUCT(1);
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245 auto feature = new FEATURE_STRUCT(&IntFeatDesc);
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246
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247 feature->Params[GeoBottom] = sample->geo_feature(GeoBottom);
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248 feature->Params[GeoTop] = sample->geo_feature(GeoTop);
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249 feature->Params[GeoWidth] = sample->geo_feature(GeoWidth);
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250 AddFeature(feature_set, feature);
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251 delete sample;
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252
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253 return feature_set;
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254 } /* ExtractIntGeoFeatures */
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255
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256 } // namespace tesseract.