Mercurial > hgrepos > Python2 > PyMuPDF

annotate mupdf-source/thirdparty/tesseract/src/lstm/input.cpp @ 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 // File: input.cpp
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3 // Description: Input layer class for neural network implementations.
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4 // Author: Ray Smith
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5 //
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6 // (C) Copyright 2014, 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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18 #include "input.h"
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20 #include <allheaders.h>
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21 #include "imagedata.h"
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22 #include "pageres.h"
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23 #include "scrollview.h"
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25 namespace tesseract {
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27 // Max height for variable height inputs before scaling anyway.
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28 const int kMaxInputHeight = 48;
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30 Input::Input(const std::string &name, int ni, int no)
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31 : Network(NT_INPUT, name, ni, no), cached_x_scale_(1) {}
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32 Input::Input(const std::string &name, const StaticShape &shape)
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33 : Network(NT_INPUT, name, shape.height(), shape.depth()), shape_(shape), cached_x_scale_(1) {
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34 if (shape.height() == 1) {
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35 ni_ = shape.depth();
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36 }
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37 }
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39 // Writes to the given file. Returns false in case of error.
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40 bool Input::Serialize(TFile *fp) const {
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41 return Network::Serialize(fp) && shape_.Serialize(fp);
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42 }
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44 // Reads from the given file. Returns false in case of error.
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45 bool Input::DeSerialize(TFile *fp) {
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46 return shape_.DeSerialize(fp);
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47 }
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49 // Returns an integer reduction factor that the network applies to the
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50 // time sequence. Assumes that any 2-d is already eliminated. Used for
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51 // scaling bounding boxes of truth data.
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52 int Input::XScaleFactor() const {
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53 return 1;
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54 }
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56 // Provides the (minimum) x scale factor to the network (of interest only to
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57 // input units) so they can determine how to scale bounding boxes.
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58 void Input::CacheXScaleFactor(int factor) {
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59 cached_x_scale_ = factor;
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60 }
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62 // Runs forward propagation of activations on the input line.
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63 // See Network for a detailed discussion of the arguments.
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64 void Input::Forward(bool debug, const NetworkIO &input, const TransposedArray *input_transpose,
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65 NetworkScratch *scratch, NetworkIO *output) {
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66 *output = input;
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67 }
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69 // Runs backward propagation of errors on the deltas line.
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70 // See NetworkCpp for a detailed discussion of the arguments.
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71 bool Input::Backward(bool debug, const NetworkIO &fwd_deltas, NetworkScratch *scratch,
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72 NetworkIO *back_deltas) {
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73 tprintf("Input::Backward should not be called!!\n");
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74 return false;
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75 }
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76
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77 // Creates and returns a Pix of appropriate size for the network from the
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78 // image_data. If non-null, *image_scale returns the image scale factor used.
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79 // Returns nullptr on error.
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80 /* static */
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81 Image Input::PrepareLSTMInputs(const ImageData &image_data, const Network *network, int min_width,
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82 TRand *randomizer, float *image_scale) {
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83 // Note that NumInputs() is defined as input image height.
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84 int target_height = network->NumInputs();
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85 int width, height;
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86 Image pix =
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87 image_data.PreScale(target_height, kMaxInputHeight, image_scale, &width, &height, nullptr);
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88 if (pix == nullptr) {
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89 tprintf("Bad pix from ImageData!\n");
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90 return nullptr;
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91 }
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92 if (width < min_width || height < min_width) {
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93 tprintf("Image too small to scale!! (%dx%d vs min width of %d)\n", width, height, min_width);
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94 pix.destroy();
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95 return nullptr;
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96 }
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97 return pix;
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98 }
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99
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100 // Converts the given pix to a NetworkIO of height and depth appropriate to the
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101 // given StaticShape:
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102 // If depth == 3, convert to 24 bit color, otherwise normalized grey.
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103 // Scale to target height, if the shape's height is > 1, or its depth if the
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104 // height == 1. If height == 0 then no scaling.
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105 // NOTE: It isn't safe for multiple threads to call this on the same pix.
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106 /* static */
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107 void Input::PreparePixInput(const StaticShape &shape, const Image pix, TRand *randomizer,
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108 NetworkIO *input) {
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109 bool color = shape.depth() == 3;
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110 Image var_pix = pix;
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111 int depth = pixGetDepth(var_pix);
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112 Image normed_pix = nullptr;
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113 // On input to BaseAPI, an image is forced to be 1, 8 or 24 bit, without
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114 // colormap, so we just have to deal with depth conversion here.
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115 if (color) {
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116 // Force RGB.
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117 if (depth == 32) {
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118 normed_pix = var_pix.clone();
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119 } else {
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120 normed_pix = pixConvertTo32(var_pix);
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121 }
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122 } else {
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123 // Convert non-8-bit images to 8 bit.
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124 if (depth == 8) {
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125 normed_pix = var_pix.clone();
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126 } else {
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127 normed_pix = pixConvertTo8(var_pix, false);
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128 }
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129 }
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130 int height = pixGetHeight(normed_pix);
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131 int target_height = shape.height();
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132 if (target_height == 1) {
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133 target_height = shape.depth();
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134 }
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135 if (target_height != 0 && target_height != height) {
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136 // Get the scaled image.
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137 float im_factor = static_cast<float>(target_height) / height;
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138 Image scaled_pix = pixScale(normed_pix, im_factor, im_factor);
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139 normed_pix.destroy();
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140 normed_pix = scaled_pix;
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141 }
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142 input->FromPix(shape, normed_pix, randomizer);
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143 normed_pix.destroy();
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144 }
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145
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146 } // namespace tesseract.