githubmirror/tesseract

src/classify/blobclass.cpp
116 строк4 KB
Stefan Weil
Remove more unused function parameters
21 июн 2026, 23:28
21 июн 2026, 23:2805fd161
/******************************************************************************
 **      Filename:       blobclass.c
 **      Purpose:        High level blob classification and training routines.
 **      Author:         Dan Johnson
 **
 **      (c) Copyright Hewlett-Packard Company, 1988.
 ** 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.
 ******************************************************************************/

#include <cstdio>

#include "classify.h"
#include "featdefs.h"
#include "mfdefs.h"   // MicroFeatureParameter
#include "mfx.h"      // BlobMicroFeatures
#include "normfeat.h"

namespace tesseract {

/*----------------------------------------------------------------------------
              Private Code
----------------------------------------------------------------------------*/
/**
 * Call the old micro-feature extractor and then copy
 * the features into the new format.  Then deallocate the
 * old micro-features.
 * @param Blob  blob to extract micro-features from
 * @param cn_denorm  control parameter to feature extractor.
 * @return Micro-features for Blob.
 */
static FEATURE_SET ExtractMicros(TBLOB *Blob, const DENORM &cn_denorm) {
  auto features = BlobMicroFeatures(Blob, cn_denorm);
  if (features.empty()) {
    return nullptr;
  }
  int n = 0;
  for ([[maybe_unused]] auto &f: features) {
    ++n;
  }
  auto FeatureSet = new FEATURE_SET_STRUCT(n);

  for (auto &f : features) {
    auto Feature = new FEATURE_STRUCT(&MicroFeatureDesc);
    for (int i = 0; i < static_cast<int>(MicroFeatureParameter::MFCount); ++i)
      Feature->Params[i] = f[i];
    // Bulge features are deprecated and should not be used. Set to 0.
    Feature->Params[static_cast<int>(MicroFeatureParameter::MFBulge1)] = 0.0f;
    Feature->Params[static_cast<int>(MicroFeatureParameter::MFBulge2)] = 0.0f;

#ifndef _WIN32
    // Assert that feature parameters are well defined.
    for (int i = 0; i < Feature->Type->NumParams; i++) {
      ASSERT_HOST(!std::isnan(Feature->Params[i]));
    }
#endif

    AddFeature(FeatureSet, Feature);
  }
  return FeatureSet;
} /* ExtractMicros */

/*---------------------------------------------------------------------------*/

// Extracts features from the given blob and saves them in the tr_file_data_
// member variable.
// fontname:  Name of font that this blob was printed in.
// cn_denorm: Character normalization transformation to apply to the blob.
// fx_info:   Character normalization parameters computed with cn_denorm.
// blob_text: Ground truth text for the blob.
void Classify::LearnBlob(const std::string &fontname, TBLOB *blob, const DENORM &cn_denorm,
                         const INT_FX_RESULT_STRUCT &fx_info, const char *blob_text) {
  std::unique_ptr<CHAR_DESC_STRUCT> CharDesc(new CHAR_DESC_STRUCT(feature_defs_));
  CharDesc->FeatureSets[0] = ExtractMicros(blob, cn_denorm);
  CharDesc->FeatureSets[1] = ExtractCharNormFeatures(fx_info);
  CharDesc->FeatureSets[2] = ExtractIntCNFeatures(*blob, fx_info);
  CharDesc->FeatureSets[3] = ExtractIntGeoFeatures(*blob, fx_info);

  if (ValidCharDescription(CharDesc.get())) {
    // Label the features with a class name and font name.
    tr_file_data_ += "\n";
    tr_file_data_ += fontname;
    tr_file_data_ += " ";
    tr_file_data_ += blob_text;
    tr_file_data_ += "\n";

    // write micro-features to file and clean up
    WriteCharDescription(feature_defs_, CharDesc.get(), tr_file_data_);
  } else {
    tprintf("Blob learned was invalid!\n");
  }
} // LearnBlob

// Writes stored training data to a .tr file based on the given filename.
// Returns false on error.
bool Classify::WriteTRFile(const char *filename) {
  bool result = false;
  std::string tr_filename = filename;
  tr_filename += ".tr";
  FILE *fp = fopen(tr_filename.c_str(), "wb");
  if (fp) {
    result = tesseract::Serialize(fp, &tr_file_data_[0], tr_file_data_.length());
    fclose(fp);
  }
  tr_file_data_.resize(0);
  return result;
}

} // namespace tesseract