Mercurial > hgrepos > Python2 > PyMuPDF
view mupdf-source/thirdparty/tesseract/src/classify/blobclass.cpp @ 46:7ee69f120f19 default tip
>>>>> 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 |
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/****************************************************************************** ** 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 "mf.h" #include "normfeat.h" namespace tesseract { /*---------------------------------------------------------------------------*/ // 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(feature_defs_, 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
