Mercurial > hgrepos > Python2 > PyMuPDF
annotate mupdf-source/thirdparty/tesseract/src/training/mergenf.cpp @ 46:7ee69f120f19 default tip
>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
| author | Franz Glasner <fzglas.hg@dom66.de> |
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| date | Sat, 11 Oct 2025 17:17:30 +0200 |
| parents | b50eed0cc0ef |
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| rev | line source |
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1 /****************************************************************************** |
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2 ** Filename: MergeNF.c |
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3 ** Purpose: Program for merging similar nano-feature protos |
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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 #define _USE_MATH_DEFINES // for M_PI |
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19 #include <algorithm> |
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20 #include <cfloat> // for FLT_MAX |
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21 #include <cmath> // for M_PI |
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22 #include <cstdio> |
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23 #include <cstring> |
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24 |
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25 #include "cluster.h" |
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26 #include "clusttool.h" |
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27 #include "featdefs.h" |
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28 #include "intproto.h" |
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29 #include "mergenf.h" |
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30 #include "ocrfeatures.h" |
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31 #include "oldlist.h" |
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32 #include "params.h" |
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33 #include "protos.h" |
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34 |
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35 using namespace tesseract; |
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36 |
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37 /*-------------------once in subfeat---------------------------------*/ |
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38 static double_VAR(training_angle_match_scale, 1.0, "Angle Match Scale ..."); |
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39 |
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40 static double_VAR(training_similarity_midpoint, 0.0075, "Similarity Midpoint ..."); |
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41 |
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42 static double_VAR(training_similarity_curl, 2.0, "Similarity Curl ..."); |
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43 |
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44 /*-----------------------------once in |
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45 * fasttrain----------------------------------*/ |
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46 static double_VAR(training_tangent_bbox_pad, 0.5, "Tangent bounding box pad ..."); |
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47 |
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48 static double_VAR(training_orthogonal_bbox_pad, 2.5, "Orthogonal bounding box pad ..."); |
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49 |
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50 static double_VAR(training_angle_pad, 45.0, "Angle pad ..."); |
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51 |
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52 /** |
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53 * Compare protos p1 and p2 and return an estimate of the |
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54 * worst evidence rating that will result for any part of p1 |
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55 * that is compared to p2. In other words, if p1 were broken |
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56 * into pico-features and each pico-feature was matched to p2, |
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57 * what is the worst evidence rating that will be achieved for |
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58 * any pico-feature. |
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59 * |
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60 * @param p1, p2 protos to be compared |
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61 * |
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62 * Globals: none |
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63 * |
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64 * @return Worst possible result when matching p1 to p2. |
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65 */ |
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66 float CompareProtos(PROTO_STRUCT *p1, PROTO_STRUCT *p2) { |
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67 float WorstEvidence = WORST_EVIDENCE; |
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68 float Evidence; |
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69 float Angle, Length; |
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70 |
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71 /* if p1 and p2 are not close in length, don't let them match */ |
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72 Length = std::fabs(p1->Length - p2->Length); |
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73 if (Length > MAX_LENGTH_MISMATCH) { |
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74 return (0.0); |
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75 } |
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76 |
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77 /* create a dummy pico-feature to be used for comparisons */ |
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78 auto Feature = new FEATURE_STRUCT(&PicoFeatDesc); |
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79 Feature->Params[PicoFeatDir] = p1->Angle; |
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80 |
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81 /* convert angle to radians */ |
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82 Angle = p1->Angle * 2.0 * M_PI; |
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83 |
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84 /* find distance from center of p1 to 1/2 picofeat from end */ |
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85 Length = p1->Length / 2.0 - GetPicoFeatureLength() / 2.0; |
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86 if (Length < 0) { |
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87 Length = 0; |
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88 } |
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89 |
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|
90 /* set the dummy pico-feature at one end of p1 and match it to p2 */ |
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91 Feature->Params[PicoFeatX] = p1->X + std::cos(Angle) * Length; |
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92 Feature->Params[PicoFeatY] = p1->Y + std::sin(Angle) * Length; |
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93 if (DummyFastMatch(Feature, p2)) { |
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94 Evidence = SubfeatureEvidence(Feature, p2); |
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95 if (Evidence < WorstEvidence) { |
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96 WorstEvidence = Evidence; |
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97 } |
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|
98 } else { |
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99 delete Feature; |
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100 return 0.0; |
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101 } |
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102 |
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103 /* set the dummy pico-feature at the other end of p1 and match it to p2 */ |
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104 Feature->Params[PicoFeatX] = p1->X - std::cos(Angle) * Length; |
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105 Feature->Params[PicoFeatY] = p1->Y - std::sin(Angle) * Length; |
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106 if (DummyFastMatch(Feature, p2)) { |
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107 Evidence = SubfeatureEvidence(Feature, p2); |
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108 if (Evidence < WorstEvidence) { |
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109 WorstEvidence = Evidence; |
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110 } |
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111 } else { |
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112 delete Feature; |
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113 return 0.0; |
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114 } |
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115 |
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116 delete Feature; |
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117 return (WorstEvidence); |
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118 |
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119 } /* CompareProtos */ |
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120 |
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121 /** |
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122 * This routine computes a proto which is the weighted |
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123 * average of protos p1 and p2. The new proto is returned |
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124 * in MergedProto. |
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125 * |
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126 * @param p1, p2 protos to be merged |
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127 * @param w1, w2 weight of each proto |
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128 * @param MergedProto place to put resulting merged proto |
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129 */ |
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130 void ComputeMergedProto(PROTO_STRUCT *p1, PROTO_STRUCT *p2, float w1, float w2, PROTO_STRUCT *MergedProto) { |
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131 float TotalWeight; |
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132 |
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133 TotalWeight = w1 + w2; |
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134 w1 /= TotalWeight; |
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135 w2 /= TotalWeight; |
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136 |
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137 MergedProto->X = p1->X * w1 + p2->X * w2; |
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138 MergedProto->Y = p1->Y * w1 + p2->Y * w2; |
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139 MergedProto->Length = p1->Length * w1 + p2->Length * w2; |
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140 MergedProto->Angle = p1->Angle * w1 + p2->Angle * w2; |
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141 FillABC(MergedProto); |
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142 } /* ComputeMergedProto */ |
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143 |
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144 /** |
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145 * This routine searches through all of the prototypes in |
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146 * Class and returns the id of the proto which would provide |
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147 * the best approximation of Prototype. If no close |
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148 * approximation can be found, NO_PROTO is returned. |
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149 * |
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150 * @param Class class to search for matching old proto in |
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151 * @param NumMerged # of protos merged into each proto of Class |
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152 * @param Prototype new proto to find match for |
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153 * |
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154 * Globals: none |
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155 * |
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156 * @return Id of closest proto in Class or NO_PROTO. |
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157 */ |
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158 int FindClosestExistingProto(CLASS_TYPE Class, int NumMerged[], PROTOTYPE *Prototype) { |
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159 PROTO_STRUCT NewProto; |
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160 PROTO_STRUCT MergedProto; |
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161 int Pid; |
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162 PROTO_STRUCT *Proto; |
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163 int BestProto; |
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|
164 float BestMatch; |
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165 float Match, OldMatch, NewMatch; |
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166 |
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|
167 MakeNewFromOld(&NewProto, Prototype); |
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168 |
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169 BestProto = NO_PROTO; |
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170 BestMatch = WORST_MATCH_ALLOWED; |
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171 for (Pid = 0; Pid < Class->NumProtos; Pid++) { |
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172 Proto = ProtoIn(Class, Pid); |
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173 ComputeMergedProto(Proto, &NewProto, static_cast<float>(NumMerged[Pid]), 1.0, &MergedProto); |
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174 OldMatch = CompareProtos(Proto, &MergedProto); |
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|
175 NewMatch = CompareProtos(&NewProto, &MergedProto); |
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176 Match = std::min(OldMatch, NewMatch); |
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177 if (Match > BestMatch) { |
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178 BestProto = Pid; |
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179 BestMatch = Match; |
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180 } |
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181 } |
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182 return BestProto; |
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183 } /* FindClosestExistingProto */ |
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184 |
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185 /** |
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186 * This fills in the fields of the New proto based on the |
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187 * fields of the Old proto. |
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188 * |
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189 * @param New new proto to be filled in |
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190 * @param Old old proto to be converted |
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191 * |
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192 * Globals: none |
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193 */ |
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194 void MakeNewFromOld(PROTO_STRUCT *New, PROTOTYPE *Old) { |
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195 New->X = CenterX(Old->Mean); |
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196 New->Y = CenterY(Old->Mean); |
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197 New->Length = LengthOf(Old->Mean); |
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198 New->Angle = OrientationOf(Old->Mean); |
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199 FillABC(New); |
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200 } /* MakeNewFromOld */ |
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201 |
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202 /*-------------------once in subfeat---------------------------------*/ |
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203 |
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204 /** |
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205 * @name SubfeatureEvidence |
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206 * |
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207 * Compare a feature to a prototype. Print the result. |
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208 */ |
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209 float SubfeatureEvidence(FEATURE Feature, PROTO_STRUCT *Proto) { |
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210 float Distance; |
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211 float Dangle; |
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212 |
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213 Dangle = Proto->Angle - Feature->Params[PicoFeatDir]; |
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214 if (Dangle < -0.5) { |
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215 Dangle += 1.0; |
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216 } |
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217 if (Dangle > 0.5) { |
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218 Dangle -= 1.0; |
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219 } |
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220 Dangle *= training_angle_match_scale; |
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221 |
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222 Distance = |
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223 Proto->A * Feature->Params[PicoFeatX] + Proto->B * Feature->Params[PicoFeatY] + Proto->C; |
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224 |
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225 return (EvidenceOf(Distance * Distance + Dangle * Dangle)); |
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226 } |
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227 |
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228 /** |
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229 * @name EvidenceOf |
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230 * |
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231 * Return the new type of evidence number corresponding to this |
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232 * distance value. This number is no longer based on the chi squared |
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233 * approximation. The equation that represents the transform is: |
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234 * 1 / (1 + (sim / midpoint) ^ curl) |
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235 */ |
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236 double EvidenceOf(double Similarity) { |
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237 Similarity /= training_similarity_midpoint; |
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238 |
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239 if (training_similarity_curl == 3) { |
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240 Similarity = Similarity * Similarity * Similarity; |
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241 } else if (training_similarity_curl == 2) { |
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242 Similarity = Similarity * Similarity; |
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243 } else { |
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244 Similarity = pow(Similarity, training_similarity_curl); |
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245 } |
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246 |
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247 return (1.0 / (1.0 + Similarity)); |
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248 } |
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249 |
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250 /** |
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251 * This routine returns true if Feature would be matched |
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252 * by a fast match table built from Proto. |
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253 * |
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254 * @param Feature feature to be "fast matched" to proto |
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255 * @param Proto proto being "fast matched" against |
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256 * |
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257 * Globals: |
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258 * - training_tangent_bbox_pad bounding box pad tangent to proto |
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259 * - training_orthogonal_bbox_pad bounding box pad orthogonal to proto |
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260 * |
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261 * @return true if feature could match Proto. |
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262 */ |
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263 bool DummyFastMatch(FEATURE Feature, PROTO_STRUCT *Proto) { |
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264 FRECT BoundingBox; |
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265 float MaxAngleError; |
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266 float AngleError; |
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267 |
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268 MaxAngleError = training_angle_pad / 360.0; |
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269 AngleError = std::fabs(Proto->Angle - Feature->Params[PicoFeatDir]); |
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270 if (AngleError > 0.5) { |
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271 AngleError = 1.0 - AngleError; |
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272 } |
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273 |
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274 if (AngleError > MaxAngleError) { |
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275 return false; |
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276 } |
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277 |
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278 ComputePaddedBoundingBox(Proto, training_tangent_bbox_pad * GetPicoFeatureLength(), |
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279 training_orthogonal_bbox_pad * GetPicoFeatureLength(), &BoundingBox); |
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280 |
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281 return PointInside(&BoundingBox, Feature->Params[PicoFeatX], Feature->Params[PicoFeatY]); |
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282 } /* DummyFastMatch */ |
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283 |
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284 /** |
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285 * This routine computes a bounding box that encloses the |
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286 * specified proto along with some padding. The |
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287 * amount of padding is specified as separate distances |
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288 * in the tangential and orthogonal directions. |
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289 * |
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290 * @param Proto proto to compute bounding box for |
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291 * @param TangentPad amount of pad to add in direction of segment |
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292 * @param OrthogonalPad amount of pad to add orthogonal to segment |
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293 * @param[out] BoundingBox place to put results |
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294 */ |
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295 void ComputePaddedBoundingBox(PROTO_STRUCT *Proto, float TangentPad, float OrthogonalPad, |
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296 FRECT *BoundingBox) { |
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297 float Length = Proto->Length / 2.0 + TangentPad; |
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298 float Angle = Proto->Angle * 2.0 * M_PI; |
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299 float CosOfAngle = fabs(std::cos(Angle)); |
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300 float SinOfAngle = fabs(std::sin(Angle)); |
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301 |
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302 float Pad = std::max(CosOfAngle * Length, SinOfAngle * OrthogonalPad); |
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303 BoundingBox->MinX = Proto->X - Pad; |
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304 BoundingBox->MaxX = Proto->X + Pad; |
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305 |
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306 Pad = std::max(SinOfAngle * Length, CosOfAngle * OrthogonalPad); |
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307 BoundingBox->MinY = Proto->Y - Pad; |
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308 BoundingBox->MaxY = Proto->Y + Pad; |
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309 |
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310 } /* ComputePaddedBoundingBox */ |
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311 |
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312 /** |
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313 * Return true if point (X,Y) is inside of Rectangle. |
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314 * |
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315 * Globals: none |
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316 * |
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317 * @return true if point (X,Y) is inside of Rectangle. |
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318 */ |
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319 bool PointInside(FRECT *Rectangle, float X, float Y) { |
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320 return (X >= Rectangle->MinX) && (X <= Rectangle->MaxX) && (Y >= Rectangle->MinY) && |
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321 (Y <= Rectangle->MaxY); |
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322 } /* PointInside */ |
