///////////////////////////////////////////////////////////////////////

// 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 char *filename) {

  if (tessedit_ambigs_training) {

    tessedit_tess_adaption_mode.set_value(0); // turn off adaption

    tessedit_enable_doc_dict.set_value(false); // turn off document dictionary

    // Explore all segmentations.

    getDict().stopper_no_acceptable_choices.set_value(true);

  }

  std::string output_fname = filename;

  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 char *filename, PAGE_RES *page_res,

                                         volatile ETEXT_DESC *monitor, FILE *output_file) {

  std::string box_fname = filename;

  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();

  std::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.

  std::vector<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