Mercurial > hgrepos > Python2 > PyMuPDF

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

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
>>>>> 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: otsuthr.cpp
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3 * Description: Simple Otsu thresholding for binarizing images.
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4 * Author: Ray Smith
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5 *
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6 * (C) Copyright 2008, Google Inc.
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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
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19 #include "otsuthr.h"
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21 #include <allheaders.h>
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22 #include <cstring>
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23 #include "helpers.h"
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25 namespace tesseract {
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27 // Computes the Otsu threshold(s) for the given image rectangle, making one
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28 // for each channel. Each channel is always one byte per pixel.
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29 // Returns an array of threshold values and an array of hi_values, such
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30 // that a pixel value >threshold[channel] is considered foreground if
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31 // hi_values[channel] is 0 or background if 1. A hi_value of -1 indicates
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32 // that there is no apparent foreground. At least one hi_value will not be -1.
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33 // The return value is the number of channels in the input image, being
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34 // the size of the output thresholds and hi_values arrays.
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35 int OtsuThreshold(Image src_pix, int left, int top, int width, int height, std::vector<int> &thresholds,
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36 std::vector<int> &hi_values) {
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37 int num_channels = pixGetDepth(src_pix) / 8;
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38 // Of all channels with no good hi_value, keep the best so we can always
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39 // produce at least one answer.
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40 int best_hi_value = 1;
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41 int best_hi_index = 0;
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42 bool any_good_hivalue = false;
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43 double best_hi_dist = 0.0;
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44 thresholds.resize(num_channels);
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45 hi_values.resize(num_channels);
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46
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47 for (int ch = 0; ch < num_channels; ++ch) {
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48 thresholds[ch] = -1;
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49 hi_values[ch] = -1;
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50 // Compute the histogram of the image rectangle.
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51 int histogram[kHistogramSize];
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52 HistogramRect(src_pix, ch, left, top, width, height, histogram);
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53 int H;
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54 int best_omega_0;
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55 int best_t = OtsuStats(histogram, &H, &best_omega_0);
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56 if (best_omega_0 == 0 || best_omega_0 == H) {
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57 // This channel is empty.
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58 continue;
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59 }
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60 // To be a convincing foreground we must have a small fraction of H
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61 // or to be a convincing background we must have a large fraction of H.
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62 // In between we assume this channel contains no thresholding information.
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63 int hi_value = best_omega_0 < H * 0.5;
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64 thresholds[ch] = best_t;
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65 if (best_omega_0 > H * 0.75) {
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66 any_good_hivalue = true;
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67 hi_values[ch] = 0;
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68 } else if (best_omega_0 < H * 0.25) {
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69 any_good_hivalue = true;
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70 hi_values[ch] = 1;
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71 } else {
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72 // In case all channels are like this, keep the best of the bad lot.
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73 double hi_dist = hi_value ? (H - best_omega_0) : best_omega_0;
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74 if (hi_dist > best_hi_dist) {
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75 best_hi_dist = hi_dist;
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76 best_hi_value = hi_value;
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77 best_hi_index = ch;
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78 }
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79 }
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80 }
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81
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82 if (!any_good_hivalue) {
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83 // Use the best of the ones that were not good enough.
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84 hi_values[best_hi_index] = best_hi_value;
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85 }
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86 return num_channels;
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87 }
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88
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89 // Computes the histogram for the given image rectangle, and the given
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90 // single channel. Each channel is always one byte per pixel.
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91 // Histogram is always a kHistogramSize(256) element array to count
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92 // occurrences of each pixel value.
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93 void HistogramRect(Image src_pix, int channel, int left, int top, int width, int height,
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94 int *histogram) {
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95 int num_channels = pixGetDepth(src_pix) / 8;
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96 channel = ClipToRange(channel, 0, num_channels - 1);
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97 int bottom = top + height;
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98 memset(histogram, 0, sizeof(*histogram) * kHistogramSize);
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99 int src_wpl = pixGetWpl(src_pix);
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100 l_uint32 *srcdata = pixGetData(src_pix);
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101 for (int y = top; y < bottom; ++y) {
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102 const l_uint32 *linedata = srcdata + y * src_wpl;
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103 for (int x = 0; x < width; ++x) {
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104 int pixel = GET_DATA_BYTE(linedata, (x + left) * num_channels + channel);
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105 ++histogram[pixel];
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106 }
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107 }
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108 }
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109
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110 // Computes the Otsu threshold(s) for the given histogram.
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111 // Also returns H = total count in histogram, and
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112 // omega0 = count of histogram below threshold.
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113 int OtsuStats(const int *histogram, int *H_out, int *omega0_out) {
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114 int H = 0;
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115 double mu_T = 0.0;
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116 for (int i = 0; i < kHistogramSize; ++i) {
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117 H += histogram[i];
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118 mu_T += static_cast<double>(i) * histogram[i];
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119 }
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120
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121 // Now maximize sig_sq_B over t.
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122 // http://www.ctie.monash.edu.au/hargreave/Cornall_Terry_328.pdf
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123 int best_t = -1;
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124 int omega_0, omega_1;
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125 int best_omega_0 = 0;
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126 double best_sig_sq_B = 0.0;
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127 double mu_0, mu_1, mu_t;
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128 omega_0 = 0;
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129 mu_t = 0.0;
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130 for (int t = 0; t < kHistogramSize - 1; ++t) {
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131 omega_0 += histogram[t];
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132 mu_t += t * static_cast<double>(histogram[t]);
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133 if (omega_0 == 0) {
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134 continue;
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135 }
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136 omega_1 = H - omega_0;
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137 if (omega_1 == 0) {
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138 break;
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139 }
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140 mu_0 = mu_t / omega_0;
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141 mu_1 = (mu_T - mu_t) / omega_1;
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142 double sig_sq_B = mu_1 - mu_0;
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143 sig_sq_B *= sig_sq_B * omega_0 * omega_1;
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144 if (best_t < 0 || sig_sq_B > best_sig_sq_B) {
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145 best_sig_sq_B = sig_sq_B;
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146 best_t = t;
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147 best_omega_0 = omega_0;
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148 }
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149 }
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150 if (H_out != nullptr) {
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151 *H_out = H;
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152 }
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153 if (omega0_out != nullptr) {
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154 *omega0_out = best_omega_0;
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155 }
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156 return best_t;
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157 }
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158
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159 } // namespace tesseract.