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https://github.com/tesseract-ocr/tesseract.git
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53fc4456cc
Eliminated the flexfx scheme for calling global feature extractor functions through an array of function pointers. Deleted dead code I found as a by-product. This CL does not change BlobToTrainingSample or ExtractFeatures to be full members of Classify (the eventual goal) as that would make it even bigger, since there are a lot of callers to these functions. When ExtractFeatures and BlobToTrainingSample are members of Classify they will be able to access control parameters in Classify, which will greatly simplify developing variations to the feature extraction process.
109 lines
4.5 KiB
C++
109 lines
4.5 KiB
C++
/******************************************************************************
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** Filename: blobclass.c
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** Purpose: High level blob classification and training routines.
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** Author: Dan Johnson
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** History: 7/21/89, DSJ, Created.
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**
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** (c) Copyright Hewlett-Packard Company, 1988.
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** Licensed under the Apache License, Version 2.0 (the "License");
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** you may not use this file except in compliance with the License.
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** You may obtain a copy of the License at
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** http://www.apache.org/licenses/LICENSE-2.0
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** Unless required by applicable law or agreed to in writing, software
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** distributed under the License is distributed on an "AS IS" BASIS,
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** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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** See the License for the specific language governing permissions and
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** limitations under the License.
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******************************************************************************/
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/**----------------------------------------------------------------------------
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Include Files and Type Defines
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----------------------------------------------------------------------------**/
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#include "blobclass.h"
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#include <stdio.h>
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#include "classify.h"
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#include "efio.h"
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#include "featdefs.h"
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#include "mf.h"
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#include "normfeat.h"
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static const char kUnknownFontName[] = "UnknownFont";
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STRING_VAR(classify_font_name, kUnknownFontName,
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"Default font name to be used in training");
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namespace tesseract {
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/**----------------------------------------------------------------------------
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Public Code
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----------------------------------------------------------------------------**/
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// Finds the name of the training font and returns it in fontname, by cutting
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// it out based on the expectation that the filename is of the form:
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// /path/to/dir/[lang].[fontname].exp[num]
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// The [lang], [fontname] and [num] fields should not have '.' characters.
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// If the global parameter classify_font_name is set, its value is used instead.
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void ExtractFontName(const STRING& filename, STRING* fontname) {
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*fontname = classify_font_name;
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if (*fontname == kUnknownFontName) {
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// filename is expected to be of the form [lang].[fontname].exp[num]
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// The [lang], [fontname] and [num] fields should not have '.' characters.
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const char *basename = strrchr(filename.string(), '/');
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const char *firstdot = strchr(basename ? basename : filename.string(), '.');
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const char *lastdot = strrchr(filename.string(), '.');
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if (firstdot != lastdot && firstdot != NULL && lastdot != NULL) {
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++firstdot;
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*fontname = firstdot;
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fontname->truncate_at(lastdot - firstdot);
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}
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}
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}
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/*---------------------------------------------------------------------------*/
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// Extracts features from the given blob and saves them in the tr_file_data_
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// member variable.
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// fontname: Name of font that this blob was printed in.
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// cn_denorm: Character normalization transformation to apply to the blob.
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// fx_info: Character normalization parameters computed with cn_denorm.
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// blob_text: Ground truth text for the blob.
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void Classify::LearnBlob(const STRING& fontname, TBLOB* blob,
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const DENORM& cn_denorm,
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const INT_FX_RESULT_STRUCT& fx_info,
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const char* blob_text) {
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CHAR_DESC CharDesc = NewCharDescription(feature_defs_);
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CharDesc->FeatureSets[0] = ExtractMicros(blob, cn_denorm);
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CharDesc->FeatureSets[1] = ExtractCharNormFeatures(fx_info);
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CharDesc->FeatureSets[2] = ExtractIntCNFeatures(*blob, fx_info);
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CharDesc->FeatureSets[3] = ExtractIntGeoFeatures(*blob, fx_info);
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if (ValidCharDescription(feature_defs_, CharDesc)) {
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// Label the features with a class name and font name.
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tr_file_data_ += "\n";
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tr_file_data_ += fontname;
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tr_file_data_ += " ";
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tr_file_data_ += blob_text;
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tr_file_data_ += "\n";
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// write micro-features to file and clean up
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WriteCharDescription(feature_defs_, CharDesc, &tr_file_data_);
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} else {
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tprintf("Blob learned was invalid!\n");
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}
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FreeCharDescription(CharDesc);
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} // LearnBlob
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// Writes stored training data to a .tr file based on the given filename.
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// Returns false on error.
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bool Classify::WriteTRFile(const STRING& filename) {
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STRING tr_filename = filename + ".tr";
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FILE* fp = Efopen(tr_filename.string(), "wb");
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int len = tr_file_data_.length();
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bool result =
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fwrite(&tr_file_data_[0], sizeof(tr_file_data_[0]), len, fp) == len;
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fclose(fp);
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tr_file_data_.truncate_at(0);
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return result;
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}
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} // namespace tesseract.
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