tesseract/src/ccmain/recogtraining.cpp
Stefan Weil 994ec697d8 Remove member functions STRING::string and StringParam::string
They were redundant because there exist member functions 'c_str' which do the same.

Signed-off-by: Stefan Weil <sw@weilnetz.de>
2019-09-23 08:33:08 +02:00

239 lines
8.9 KiB
C++

///////////////////////////////////////////////////////////////////////
// File: recogtraining.cpp
// Description: Functions for ambiguity and parameter training.
// Author: Daria Antonova
//
// (C) Copyright 2009, Google Inc.
// 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 "tesseractclass.h"
#include "boxread.h"
#include "control.h"
#include "host.h" // for NearlyEqual
#include "ratngs.h"
#ifndef DISABLED_LEGACY_ENGINE
#include "reject.h"
#endif
#include "stopper.h"
namespace tesseract {
const int16_t kMaxBoxEdgeDiff = 2;
// Sets flags necessary for recognition in the training mode.
// Opens and returns the pointer to the output file.
FILE* Tesseract::init_recog_training(const STRING& fname) {
if (tessedit_ambigs_training) {
tessedit_tess_adaption_mode.set_value(0); // turn off adaption
tessedit_enable_doc_dict.set_value(0); // turn off document dictionary
// Explore all segmentations.
getDict().stopper_no_acceptable_choices.set_value(1);
}
STRING output_fname = fname;
const char* lastdot = strrchr(output_fname.c_str(), '.');
if (lastdot != nullptr)
output_fname[lastdot - output_fname.c_str()] = '\0';
output_fname += ".txt";
FILE* output_file = fopen(output_fname.c_str(), "a+");
if (output_file == nullptr) {
tprintf("Error: Could not open file %s\n", output_fname.c_str());
ASSERT_HOST(output_file);
}
return output_file;
}
// Copies the bounding box from page_res_it->word() to the given TBOX.
static bool read_t(PAGE_RES_IT* page_res_it, TBOX* tbox) {
while (page_res_it->block() != nullptr && page_res_it->word() == nullptr)
page_res_it->forward();
if (page_res_it->word() != nullptr) {
*tbox = page_res_it->word()->word->bounding_box();
// If tbox->left() is negative, the training image has vertical text and
// all the coordinates of bounding boxes of page_res are rotated by 90
// degrees in a counterclockwise direction. We need to rotate the TBOX back
// in order to compare with the TBOXes of box files.
if (tbox->left() < 0) {
tbox->rotate(FCOORD(0.0, -1.0));
}
return true;
} else {
return false;
}
}
// This function takes tif/box pair of files and runs recognition on the image,
// while making sure that the word bounds that tesseract identified roughly
// match to those specified by the input box file. For each word (ngram in a
// single bounding box from the input box file) it outputs the ocred result,
// the correct label, rating and certainty.
void Tesseract::recog_training_segmented(const STRING& fname,
PAGE_RES* page_res,
volatile ETEXT_DESC* monitor,
FILE* output_file) {
STRING box_fname = fname;
const char* lastdot = strrchr(box_fname.c_str(), '.');
if (lastdot != nullptr)
box_fname[lastdot - box_fname.c_str()] = '\0';
box_fname += ".box";
// ReadNextBox() will close box_file
FILE* box_file = fopen(box_fname.c_str(), "r");
if (box_file == nullptr) {
tprintf("Error: Could not open file %s\n", box_fname.c_str());
ASSERT_HOST(box_file);
}
PAGE_RES_IT page_res_it;
page_res_it.page_res = page_res;
page_res_it.restart_page();
STRING label;
// Process all the words on this page.
TBOX tbox; // tesseract-identified box
TBOX bbox; // box from the box file
bool keep_going;
int line_number = 0;
int examined_words = 0;
do {
keep_going = read_t(&page_res_it, &tbox);
keep_going &=
ReadNextBox(applybox_page, &line_number, box_file, &label, &bbox);
// Align bottom left points of the TBOXes.
while (keep_going &&
!NearlyEqual<int>(tbox.bottom(), bbox.bottom(), kMaxBoxEdgeDiff)) {
if (bbox.bottom() < tbox.bottom()) {
page_res_it.forward();
keep_going = read_t(&page_res_it, &tbox);
} else {
keep_going =
ReadNextBox(applybox_page, &line_number, box_file, &label, &bbox);
}
}
while (keep_going &&
!NearlyEqual<int>(tbox.left(), bbox.left(), kMaxBoxEdgeDiff)) {
if (bbox.left() > tbox.left()) {
page_res_it.forward();
keep_going = read_t(&page_res_it, &tbox);
} else {
keep_going =
ReadNextBox(applybox_page, &line_number, box_file, &label, &bbox);
}
}
// OCR the word if top right points of the TBOXes are similar.
if (keep_going &&
NearlyEqual<int>(tbox.right(), bbox.right(), kMaxBoxEdgeDiff) &&
NearlyEqual<int>(tbox.top(), bbox.top(), kMaxBoxEdgeDiff)) {
ambigs_classify_and_output(label.c_str(), &page_res_it, output_file);
examined_words++;
}
page_res_it.forward();
} while (keep_going);
// Set up scripts on all of the words that did not get sent to
// ambigs_classify_and_output. They all should have, but if all the
// werd_res's don't get uch_sets, tesseract will crash when you try
// to iterate over them. :-(
int total_words = 0;
for (page_res_it.restart_page(); page_res_it.block() != nullptr;
page_res_it.forward()) {
if (page_res_it.word()) {
if (page_res_it.word()->uch_set == nullptr)
page_res_it.word()->SetupFake(unicharset);
total_words++;
}
}
if (examined_words < 0.85 * total_words) {
tprintf(
"TODO(antonova): clean up recog_training_segmented; "
" It examined only a small fraction of the ambigs image.\n");
}
tprintf("recog_training_segmented: examined %d / %d words.\n", examined_words,
total_words);
}
// Helper prints the given set of blob choices.
static void PrintPath(int length, const BLOB_CHOICE** blob_choices,
const UNICHARSET& unicharset, const char* label,
FILE* output_file) {
float rating = 0.0f;
float certainty = 0.0f;
for (int i = 0; i < length; ++i) {
const BLOB_CHOICE* blob_choice = blob_choices[i];
fprintf(output_file, "%s",
unicharset.id_to_unichar(blob_choice->unichar_id()));
rating += blob_choice->rating();
if (certainty > blob_choice->certainty())
certainty = blob_choice->certainty();
}
fprintf(output_file, "\t%s\t%.4f\t%.4f\n", label, rating, certainty);
}
// Helper recursively prints all paths through the ratings matrix, starting
// at column col.
static void PrintMatrixPaths(int col, int dim, const MATRIX& ratings,
int length, const BLOB_CHOICE** blob_choices,
const UNICHARSET& unicharset, const char* label,
FILE* output_file) {
for (int row = col; row < dim && row - col < ratings.bandwidth(); ++row) {
if (ratings.get(col, row) != NOT_CLASSIFIED) {
BLOB_CHOICE_IT bc_it(ratings.get(col, row));
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
blob_choices[length] = bc_it.data();
if (row + 1 < dim) {
PrintMatrixPaths(row + 1, dim, ratings, length + 1, blob_choices,
unicharset, label, output_file);
} else {
PrintPath(length + 1, blob_choices, unicharset, label, output_file);
}
}
}
}
}
// Runs classify_word_pass1() on the current word. Outputs Tesseract's
// raw choice as a result of the classification. For words labeled with a
// single unichar also outputs all alternatives from blob_choices of the
// best choice.
void Tesseract::ambigs_classify_and_output(const char* label,
PAGE_RES_IT* pr_it,
FILE* output_file) {
// Classify word.
fflush(stdout);
WordData word_data(*pr_it);
SetupWordPassN(1, &word_data);
classify_word_and_language(1, pr_it, &word_data);
WERD_RES* werd_res = word_data.word;
WERD_CHOICE* best_choice = werd_res->best_choice;
ASSERT_HOST(best_choice != nullptr);
// Compute the number of unichars in the label.
GenericVector<UNICHAR_ID> encoding;
if (!unicharset.encode_string(label, true, &encoding, nullptr, nullptr)) {
tprintf("Not outputting illegal unichar %s\n", label);
return;
}
// Dump all paths through the ratings matrix (which is normally small).
int dim = werd_res->ratings->dimension();
const auto** blob_choices = new const BLOB_CHOICE*[dim];
PrintMatrixPaths(0, dim, *werd_res->ratings, 0, blob_choices, unicharset,
label, output_file);
delete[] blob_choices;
}
} // namespace tesseract