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annotate mupdf-source/thirdparty/tesseract/src/ccstruct/linlsq.h @ 46:7ee69f120f19 default tip

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>>>>> tag v1.26.5+1 for changeset b74429b0f5c4
author Franz Glasner <fzglas.hg@dom66.de>
date Sat, 11 Oct 2025 17:17:30 +0200
parents b50eed0cc0ef
children
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 /**********************************************************************
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2 * File: linlsq.h (Formerly llsq.h)
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3 * Description: Linear Least squares fitting code.
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4 * Author: Ray Smith
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5 *
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6 * (C) Copyright 1991, Hewlett-Packard Ltd.
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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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19 #ifndef TESSERACT_CCSTRUCT_LINLSQ_H_
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20 #define TESSERACT_CCSTRUCT_LINLSQ_H_
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22 #include "points.h" // for FCOORD
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24 #include <algorithm> // for std::nth_element
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25 #include <cstdint> // for int32_t
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27 namespace tesseract {
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29 class TESS_API LLSQ {
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30 public:
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31 LLSQ() { // constructor
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32 clear(); // set to zeros
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33 }
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34 void clear(); // initialize
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36 // Adds an element with a weight of 1.
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37 void add(double x, double y);
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38 // Adds an element with a specified weight.
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39 void add(double x, double y, double weight);
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40 // Adds a whole LLSQ.
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41 void add(const LLSQ &other);
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42 // Deletes an element with a weight of 1.
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43 void remove(double x, double y);
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44 int32_t count() const { // no of elements
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45 return static_cast<int>(total_weight + 0.5);
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46 }
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47
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48 double m() const; // get gradient
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49 double c(double m) const; // get constant
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50 double rms(double m, double c) const; // get error
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51 double pearson() const; // get correlation coefficient.
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53 // Returns the x,y means as an FCOORD.
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54 FCOORD mean_point() const;
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56 // Returns the average sum of squared perpendicular error from a line
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57 // through mean_point() in the direction dir.
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58 double rms_orth(const FCOORD &dir) const;
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60 // Returns the direction of the fitted line as a unit vector, using the
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61 // least mean squared perpendicular distance. The line runs through the
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62 // mean_point, i.e. a point p on the line is given by:
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63 // p = mean_point() + lambda * vector_fit() for some real number lambda.
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64 // Note that the result (0<=x<=1, -1<=y<=-1) is directionally ambiguous
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65 // and may be negated without changing its meaning, since a line is only
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66 // unique to a range of pi radians.
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67 // Modernists prefer to think of this as an Eigenvalue problem, but
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68 // Pearson had the simple solution in 1901.
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69 //
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70 // Note that this is equivalent to returning the Principal Component in PCA,
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71 // or the eigenvector corresponding to the largest eigenvalue in the
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72 // covariance matrix.
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73 FCOORD vector_fit() const;
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74
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75 // Returns the covariance.
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76 double covariance() const {
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77 if (total_weight > 0.0) {
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78 return (sigxy - sigx * sigy / total_weight) / total_weight;
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79 } else {
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80 return 0.0;
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81 }
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82 }
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83 double x_variance() const {
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84 if (total_weight > 0.0) {
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85 return (sigxx - sigx * sigx / total_weight) / total_weight;
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86 } else {
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87 return 0.0;
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88 }
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89 }
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90 double y_variance() const {
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91 if (total_weight > 0.0) {
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92 return (sigyy - sigy * sigy / total_weight) / total_weight;
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93 } else {
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94 return 0.0;
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95 }
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96 }
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97
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98 private:
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99 double total_weight; // no of elements or sum of weights.
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100 double sigx; // sum of x
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101 double sigy; // sum of y
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102 double sigxx; // sum x squared
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103 double sigxy; // sum of xy
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104 double sigyy; // sum y squared
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105 };
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106
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107 // Returns the median value of the vector, given that the values are
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108 // circular, with the given modulus. Values may be signed or unsigned,
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109 // eg range from -pi to pi (modulus 2pi) or from 0 to 2pi (modulus 2pi).
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110 // NOTE that the array is shuffled, but the time taken is linear.
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111 // An assumption is made that most of the values are spread over no more than
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112 // half the range, but wrap-around is accounted for if the median is near
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113 // the wrap-around point.
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114 // Cannot be a member of vector, as it makes heavy use of LLSQ.
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115 // T must be an integer or float/double type.
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116 template <typename T>
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117 T MedianOfCircularValues(T modulus, std::vector<T> &v) {
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118 LLSQ stats;
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119 T halfrange = static_cast<T>(modulus / 2);
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120 auto num_elements = v.size();
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121 for (auto i : v) {
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122 stats.add(i, i + halfrange);
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123 }
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124 bool offset_needed = stats.y_variance() < stats.x_variance();
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125 if (offset_needed) {
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126 for (auto i : v) {
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127 i += halfrange;
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128 }
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129 }
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130 auto median_index = num_elements / 2;
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131 std::nth_element(v.begin(), v.begin() + median_index, v.end());
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132 if (offset_needed) {
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133 for (auto i : v) {
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134 i -= halfrange;
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135 }
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136 }
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137 return v[median_index];
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138 }
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139
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140 } // namespace tesseract
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141
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142 #endif // TESSERACT_CCSTRUCT_LINLSQ_H_