githubmirror/tesseract

src/classify/trainingsample.cpp
359 строк13 KB
Stefan Weil
Fix compiler warnings caused by Leptonica (-Wold-style-cast)
21 июн 2026, 23:20
21 июн 2026, 23:20c07fbb7
// Copyright 2010 Google Inc. All Rights Reserved.
// Author: rays@google.com (Ray Smith)
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
// http://www.apache.org/licenses/LICENSE-2.0
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
///////////////////////////////////////////////////////////////////////

#define _USE_MATH_DEFINES // for M_PI
// Include automatically generated configuration file if running autoconf.
#ifdef HAVE_CONFIG_H
#  include "config_auto.h"
#endif

#include "trainingsample.h"

#include "helpers.h"
#include "image.h"           // for Image
#include "intfeaturespace.h"
#include "normfeat.h"
#include "shapetable.h"

#include <cmath> // for M_PI

namespace tesseract {

// Center of randomizing operations.
const int kRandomizingCenter = 128;

// Randomizing factors.
const std::array<int, kSampleYShiftSize> TrainingSample::kYShiftValues = {6, 3, -3, -6, 0};
const std::array<double, kSampleScaleSize> TrainingSample::kScaleValues = {1.0625, 0.9375, 1.0};

TrainingSample::~TrainingSample() {
  delete[] features_;
  delete[] micro_features_;
}

// WARNING! Serialize/DeSerialize do not save/restore the "cache" data
// members, which is mostly the mapped features, and the weight.
// It is assumed these can all be reconstructed from what is saved.
// Writes to the given file. Returns false in case of error.
bool TrainingSample::Serialize(FILE *fp) const {
  if (fwrite(&class_id_, sizeof(class_id_), 1, fp) != 1) {
    return false;
  }
  if (fwrite(&font_id_, sizeof(font_id_), 1, fp) != 1) {
    return false;
  }
  if (fwrite(&page_num_, sizeof(page_num_), 1, fp) != 1) {
    return false;
  }
  if (!bounding_box_.Serialize(fp)) {
    return false;
  }
  if (fwrite(&num_features_, sizeof(num_features_), 1, fp) != 1) {
    return false;
  }
  if (fwrite(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1) {
    return false;
  }
  if (fwrite(&outline_length_, sizeof(outline_length_), 1, fp) != 1) {
    return false;
  }
  if (fwrite(features_, sizeof(*features_), num_features_, fp) != num_features_) {
    return false;
  }
  if (fwrite(micro_features_, sizeof(*micro_features_), num_micro_features_, fp) !=
      num_micro_features_) {
    return false;
  }
  if (fwrite(cn_feature_.data(), sizeof(cn_feature_[0]), kNumCNParams, fp) != kNumCNParams) {
    return false;
  }
  if (fwrite(geo_feature_.data(), sizeof(geo_feature_[0]), GeoCount, fp) != GeoCount) {
    return false;
  }
  return true;
}

// Creates from the given file. Returns nullptr in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
TrainingSample *TrainingSample::DeSerializeCreate(bool swap, FILE *fp) {
  auto *sample = new TrainingSample;
  if (sample->DeSerialize(swap, fp)) {
    return sample;
  }
  delete sample;
  return nullptr;
}

// Reads from the given file. Returns false in case of error.
// If swap is true, assumes a big/little-endian swap is needed.
bool TrainingSample::DeSerialize(bool swap, FILE *fp) {
  if (fread(&class_id_, sizeof(class_id_), 1, fp) != 1) {
    return false;
  }
  if (fread(&font_id_, sizeof(font_id_), 1, fp) != 1) {
    return false;
  }
  if (fread(&page_num_, sizeof(page_num_), 1, fp) != 1) {
    return false;
  }
  if (!bounding_box_.DeSerialize(swap, fp)) {
    return false;
  }
  if (fread(&num_features_, sizeof(num_features_), 1, fp) != 1) {
    return false;
  }
  if (fread(&num_micro_features_, sizeof(num_micro_features_), 1, fp) != 1) {
    return false;
  }
  if (fread(&outline_length_, sizeof(outline_length_), 1, fp) != 1) {
    return false;
  }
  if (swap) {
    ReverseN(&class_id_, sizeof(class_id_));
    ReverseN(&num_features_, sizeof(num_features_));
    ReverseN(&num_micro_features_, sizeof(num_micro_features_));
    ReverseN(&outline_length_, sizeof(outline_length_));
  }
  // Arbitrarily limit the number of elements to protect against bad data.
  if (num_features_ > UINT16_MAX) {
    return false;
  }
  if (num_micro_features_ > UINT16_MAX) {
    return false;
  }
  delete[] features_;
  features_ = new INT_FEATURE_STRUCT[num_features_];
  if (fread(features_, sizeof(*features_), num_features_, fp) != num_features_) {
    return false;
  }
  delete[] micro_features_;
  micro_features_ = new MicroFeature[num_micro_features_];
  if (fread(micro_features_, sizeof(*micro_features_), num_micro_features_, fp) !=
      num_micro_features_) {
    return false;
  }
  if (fread(cn_feature_.data(), sizeof(cn_feature_[0]), kNumCNParams, fp) != kNumCNParams) {
    return false;
  }
  if (fread(geo_feature_.data(), sizeof(geo_feature_[0]), GeoCount, fp) != GeoCount) {
    return false;
  }
  return true;
}

// Saves the given features into a TrainingSample.
TrainingSample *TrainingSample::CopyFromFeatures(const INT_FX_RESULT_STRUCT &fx_info,
                                                 const TBOX &bounding_box,
                                                 const INT_FEATURE_STRUCT *features,
                                                 int num_features) {
  auto *sample = new TrainingSample;
  sample->num_features_ = num_features;
  sample->features_ = new INT_FEATURE_STRUCT[num_features];
  sample->outline_length_ = fx_info.Length;
  memcpy(sample->features_, features, num_features * sizeof(features[0]));
  sample->geo_feature_[GeoBottom] = bounding_box.bottom();
  sample->geo_feature_[GeoTop] = bounding_box.top();
  sample->geo_feature_[GeoWidth] = bounding_box.width();

  // Generate the cn_feature_ from the fx_info.
  sample->cn_feature_[CharNormY] = MF_SCALE_FACTOR * (fx_info.Ymean - kBlnBaselineOffset);
  sample->cn_feature_[CharNormLength] = MF_SCALE_FACTOR * fx_info.Length / LENGTH_COMPRESSION;
  sample->cn_feature_[CharNormRx] = MF_SCALE_FACTOR * fx_info.Rx;
  sample->cn_feature_[CharNormRy] = MF_SCALE_FACTOR * fx_info.Ry;

  sample->features_are_indexed_ = false;
  sample->features_are_mapped_ = false;
  return sample;
}

// Returns the cn_feature as a FEATURE_STRUCT* needed by cntraining.
FEATURE_STRUCT *TrainingSample::GetCNFeature() const {
  auto feature = new FEATURE_STRUCT(&CharNormDesc);
  for (int i = 0; i < kNumCNParams; ++i) {
    feature->Params[i] = cn_feature_[i];
  }
  return feature;
}

// Constructs and returns a copy randomized by the method given by
// the randomizer index. If index is out of [0, kSampleRandomSize) then
// an exact copy is returned.
TrainingSample *TrainingSample::RandomizedCopy(int index) const {
  TrainingSample *sample = Copy();
  if (index >= 0 && index < kSampleRandomSize) {
    ++index; // Remove the first combination.
    const int yshift = kYShiftValues[index / kSampleScaleSize];
    double scaling = kScaleValues[index % kSampleScaleSize];
    for (uint32_t i = 0; i < num_features_; ++i) {
      double result = (features_[i].X - kRandomizingCenter) * scaling;
      result += kRandomizingCenter;
      sample->features_[i].X = ClipToRange<int>(result + 0.5, 0, UINT8_MAX);
      result = (features_[i].Y - kRandomizingCenter) * scaling;
      result += kRandomizingCenter + yshift;
      sample->features_[i].Y = ClipToRange<int>(result + 0.5, 0, UINT8_MAX);
    }
  }
  return sample;
}

// Constructs and returns an exact copy.
TrainingSample *TrainingSample::Copy() const {
  auto *sample = new TrainingSample;
  sample->class_id_ = class_id_;
  sample->font_id_ = font_id_;
  sample->weight_ = weight_;
  sample->sample_index_ = sample_index_;
  sample->num_features_ = num_features_;
  if (num_features_ > 0) {
    sample->features_ = new INT_FEATURE_STRUCT[num_features_];
    memcpy(sample->features_, features_, num_features_ * sizeof(features_[0]));
  }
  sample->num_micro_features_ = num_micro_features_;
  if (num_micro_features_ > 0) {
    sample->micro_features_ = new MicroFeature[num_micro_features_];
    memcpy(sample->micro_features_, micro_features_,
           num_micro_features_ * sizeof(micro_features_[0]));
  }
  memcpy(sample->cn_feature_.data(), cn_feature_.data(), sizeof(cn_feature_[0]) * kNumCNParams);
  memcpy(sample->geo_feature_.data(), geo_feature_.data(), sizeof(geo_feature_[0]) * GeoCount);
  return sample;
}

// Extracts the needed information from the CHAR_DESC_STRUCT.
void TrainingSample::ExtractCharDesc(int int_feature_type, int micro_type, int cn_type,
                                     int geo_type, CHAR_DESC_STRUCT *char_desc) {
  // Extract the INT features.
  delete[] features_;
  FEATURE_SET_STRUCT *char_features = char_desc->FeatureSets[int_feature_type];
  if (char_features == nullptr) {
    tprintf("Error: no features to train on of type %s\n", kIntFeatureType);
    num_features_ = 0;
    features_ = nullptr;
  } else {
    num_features_ = char_features->NumFeatures;
    features_ = new INT_FEATURE_STRUCT[num_features_];
    for (uint32_t f = 0; f < num_features_; ++f) {
      features_[f].X = static_cast<uint8_t>(char_features->Features[f]->Params[IntX]);
      features_[f].Y = static_cast<uint8_t>(char_features->Features[f]->Params[IntY]);
      features_[f].Theta = static_cast<uint8_t>(char_features->Features[f]->Params[IntDir]);
      features_[f].CP_misses = 0;
    }
  }
  // Extract the Micro features.
  delete[] micro_features_;
  char_features = char_desc->FeatureSets[micro_type];
  if (char_features == nullptr) {
    tprintf("Error: no features to train on of type %s\n", kMicroFeatureType);
    num_micro_features_ = 0;
    micro_features_ = nullptr;
  } else {
    num_micro_features_ = char_features->NumFeatures;
    micro_features_ = new MicroFeature[num_micro_features_];
    for (uint32_t f = 0; f < num_micro_features_; ++f) {
      for (int d = 0; d < static_cast<int>(MicroFeatureParameter::MFCount); ++d) {
        micro_features_[f][d] = char_features->Features[f]->Params[d];
      }
    }
  }
  // Extract the CN feature.
  char_features = char_desc->FeatureSets[cn_type];
  if (char_features == nullptr) {
    tprintf("Error: no CN feature to train on.\n");
  } else {
    ASSERT_HOST(char_features->NumFeatures == 1);
    cn_feature_[CharNormY] = char_features->Features[0]->Params[CharNormY];
    cn_feature_[CharNormLength] = char_features->Features[0]->Params[CharNormLength];
    cn_feature_[CharNormRx] = char_features->Features[0]->Params[CharNormRx];
    cn_feature_[CharNormRy] = char_features->Features[0]->Params[CharNormRy];
  }
  // Extract the Geo feature.
  char_features = char_desc->FeatureSets[geo_type];
  if (char_features == nullptr) {
    tprintf("Error: no Geo feature to train on.\n");
  } else {
    ASSERT_HOST(char_features->NumFeatures == 1);
    geo_feature_[GeoBottom] = char_features->Features[0]->Params[GeoBottom];
    geo_feature_[GeoTop] = char_features->Features[0]->Params[GeoTop];
    geo_feature_[GeoWidth] = char_features->Features[0]->Params[GeoWidth];
  }
  features_are_indexed_ = false;
  features_are_mapped_ = false;
}

// Sets the mapped_features_ from the features_ using the provided
// feature_space to the indexed versions of the features.
void TrainingSample::IndexFeatures(const IntFeatureSpace &feature_space) {
  std::vector<int> indexed_features;
  feature_space.IndexAndSortFeatures(features_, num_features_, &mapped_features_);
  features_are_indexed_ = true;
  features_are_mapped_ = false;
}

// Returns a pix representing the sample. (Int features only.)
Image TrainingSample::RenderToPix(const UNICHARSET *unicharset) const {
  Image pix = pixCreate(kIntFeatureExtent, kIntFeatureExtent, 1);
  for (uint32_t f = 0; f < num_features_; ++f) {
    int start_x = features_[f].X;
    int start_y = kIntFeatureExtent - features_[f].Y;
    double dx = cos((features_[f].Theta / 256.0) * 2.0 * M_PI - M_PI);
    double dy = -sin((features_[f].Theta / 256.0) * 2.0 * M_PI - M_PI);
    for (int i = 0; i <= 5; ++i) {
      int x = static_cast<int>(start_x + dx * i);
      int y = static_cast<int>(start_y + dy * i);
      if (x >= 0 && x < 256 && y >= 0 && y < 256) {
        pixSetPixel(pix, x, y, 1);
      }
    }
  }
  if (unicharset != nullptr) {
    pixSetText(pix, unicharset->id_to_unichar(class_id_));
  }
  return pix;
}

#ifndef GRAPHICS_DISABLED

// Displays the features in the given window with the given color.
void TrainingSample::DisplayFeatures(ScrollView::Color color, ScrollView *window) const {
  for (uint32_t f = 0; f < num_features_; ++f) {
    RenderIntFeature(window, &features_[f], color);
  }
}

#endif // !GRAPHICS_DISABLED

// Returns a pix of the original sample image. The pix is padded all round
// by padding wherever possible.
// The returned Pix must be pixDestroyed after use.
// If the input page_pix is nullptr, nullptr is returned.
Image TrainingSample::GetSamplePix(int padding, Image page_pix) const {
  if (page_pix == nullptr) {
    return nullptr;
  }
  int page_width = pixGetWidth(page_pix);
  int page_height = pixGetHeight(page_pix);
  TBOX padded_box = bounding_box();
  padded_box.pad(padding, padding);
  // Clip the padded_box to the limits of the page
  TBOX page_box(0, 0, page_width, page_height);
  padded_box &= page_box;
  Box *box =
      boxCreate(page_box.left(), page_height - page_box.top(), page_box.width(), page_box.height());
  Image sample_pix = pixClipRectangle(page_pix, box, nullptr);
  boxDestroy(&box);
  return sample_pix;
}

} // namespace tesseract