Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/classify/cluster.h @ 21:2f43e400f144

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Provide an "all" target to build both the sdist and the wheel
author Franz Glasner <fzglas.hg@dom66.de>
date Fri, 19 Sep 2025 10:28:53 +0200
parents b50eed0cc0ef
children
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1 /******************************************************************************
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2 ** Filename: cluster.h
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3 ** Purpose: Definition of feature space clustering routines
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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 #ifndef CLUSTER_H
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19 #define CLUSTER_H
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21 #include "kdtree.h"
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22 #include "oldlist.h"
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24 namespace tesseract {
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26 struct BUCKETS;
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28 #define MINBUCKETS 5
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29 #define MAXBUCKETS 39
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31 /*----------------------------------------------------------------------
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32 Types
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33 ----------------------------------------------------------------------*/
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34 struct CLUSTER {
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35 CLUSTER(size_t n) : Mean(n) {
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36 }
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38 ~CLUSTER() {
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39 delete Left;
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40 delete Right;
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41 }
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43 bool Clustered : 1; // true if included in a higher cluster
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44 bool Prototype : 1; // true if cluster represented by a proto
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45 unsigned SampleCount : 30; // number of samples in this cluster
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46 CLUSTER *Left; // ptr to left sub-cluster
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47 CLUSTER *Right; // ptr to right sub-cluster
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48 int32_t CharID; // identifier of char sample came from
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49 std::vector<float> Mean; // mean of cluster - SampleSize floats
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50 };
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51 using SAMPLE = CLUSTER; // can refer to as either sample or cluster
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53 typedef enum { spherical, elliptical, mixed, automatic } PROTOSTYLE;
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55 struct CLUSTERCONFIG { // parameters to control clustering
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56 PROTOSTYLE ProtoStyle; // specifies types of protos to be made
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57 float MinSamples; // min # of samples per proto - % of total
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58 float MaxIllegal; // max percentage of samples in a cluster which
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59 // have more than 1 feature in that cluster
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60 float Independence; // desired independence between dimensions
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61 double Confidence; // desired confidence in prototypes created
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62 int MagicSamples; // Ideal number of samples in a cluster.
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63 };
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64
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65 typedef enum { normal, uniform, D_random, DISTRIBUTION_COUNT } DISTRIBUTION;
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66
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67 union FLOATUNION {
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68 float Spherical;
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69 float *Elliptical;
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70 };
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72 struct PROTOTYPE {
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73 bool Significant : 1; // true if prototype is significant
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74 bool Merged : 1; // Merged after clustering so do not output
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75 // but kept for display purposes. If it has no
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76 // samples then it was actually merged.
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77 // Otherwise it matched an already significant
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78 // cluster.
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79 unsigned Style : 2; // spherical, elliptical, or mixed
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80 unsigned NumSamples : 28; // number of samples in the cluster
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81 CLUSTER *Cluster; // ptr to cluster which made prototype
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82 std::vector<DISTRIBUTION> Distrib; // different distribution for each dimension
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83 std::vector<float> Mean; // prototype mean
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84 float TotalMagnitude; // total magnitude over all dimensions
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85 float LogMagnitude; // log base e of TotalMagnitude
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86 FLOATUNION Variance; // prototype variance
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87 FLOATUNION Magnitude; // magnitude of density function
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88 FLOATUNION Weight; // weight of density function
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89 };
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90
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91 struct CLUSTERER {
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92 int16_t SampleSize; // number of parameters per sample
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93 PARAM_DESC *ParamDesc; // description of each parameter
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94 int32_t NumberOfSamples; // total number of samples being clustered
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95 KDTREE *KDTree; // for optimal nearest neighbor searching
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96 CLUSTER *Root; // ptr to root cluster of cluster tree
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97 LIST ProtoList; // list of prototypes
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98 uint32_t NumChar; // # of characters represented by samples
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99 // cache of reusable histograms by distribution type and number of buckets.
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100 BUCKETS *bucket_cache[DISTRIBUTION_COUNT][MAXBUCKETS + 1 - MINBUCKETS];
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101 };
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102
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103 struct SAMPLELIST {
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104 int32_t NumSamples; // number of samples in list
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105 int32_t MaxNumSamples; // maximum size of list
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106 SAMPLE *Sample[1]; // array of ptrs to sample data structures
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107 };
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108
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109 // low level cluster tree analysis routines.
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110 #define InitSampleSearch(S, C) (((C) == nullptr) ? (S = NIL_LIST) : (S = push(NIL_LIST, (C))))
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111
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112 /*--------------------------------------------------------------------------
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113 Public Function Prototypes
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114 --------------------------------------------------------------------------*/
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115 TESS_API
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116 CLUSTERER *MakeClusterer(int16_t SampleSize, const PARAM_DESC ParamDesc[]);
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117
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118 TESS_API
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119 SAMPLE *MakeSample(CLUSTERER *Clusterer, const float *Feature, uint32_t CharID);
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120
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121 TESS_API
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122 LIST ClusterSamples(CLUSTERER *Clusterer, CLUSTERCONFIG *Config);
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123
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124 TESS_API
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125 void FreeClusterer(CLUSTERER *Clusterer);
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126
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127 TESS_API
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128 void FreeProtoList(LIST *ProtoList);
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129
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130 void FreePrototype(void *arg); // PROTOTYPE *Prototype);
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131
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132 CLUSTER *NextSample(LIST *SearchState);
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133
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134 float Mean(PROTOTYPE *Proto, uint16_t Dimension);
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135
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136 float StandardDeviation(PROTOTYPE *Proto, uint16_t Dimension);
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137
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138 TESS_API
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139 int32_t MergeClusters(int16_t N, PARAM_DESC ParamDesc[], int32_t n1, int32_t n2, float m[],
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140 float m1[], float m2[]);
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141
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142 } // namespace tesseract
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143
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144 #endif