C++ BLOB_CHOICE::print_full方法代码示例

void LMPainPoints::GenerateFromPath(float rating_cert_scale,
                                    ViterbiStateEntry *vse,
                                    WERD_RES *word_res) {
  ViterbiStateEntry *curr_vse = vse;
  BLOB_CHOICE *curr_b = vse->curr_b;
  // The following pain point generation and priority calculation approaches
  // prioritize exploring paths with low average rating of the known part of
  // the path, while not relying on the ratings of the pieces to be combined.
  //
  // A pain point to combine the neighbors is generated for each pair of
  // neighboring blobs on the path (the path is represented by vse argument
  // given to GenerateFromPath()). The priority of each pain point is set to
  // the average rating (per outline length) of the path, not including the
  // ratings of the blobs to be combined.
  // The ratings of the blobs to be combined are not used to calculate the
  // priority, since it is not possible to determine from their magnitude
  // whether it will be beneficial to combine the blobs. The reason is that
  // chopped junk blobs (/ | - ') can have very good (low) ratings, however
  // combining them will be beneficial. Blobs with high ratings might be
  // over-joined pieces of characters, but also could be blobs from an unseen
  // font or chopped pieces of complex characters.
  while (curr_vse->parent_vse != NULL) {
    ViterbiStateEntry* parent_vse = curr_vse->parent_vse;
    const MATRIX_COORD& curr_cell = curr_b->matrix_cell();
    const MATRIX_COORD& parent_cell = parent_vse->curr_b->matrix_cell();
    MATRIX_COORD pain_coord(parent_cell.col, curr_cell.row);
    if (!pain_coord.Valid(*word_res->ratings) ||
        !word_res->ratings->Classified(parent_cell.col, curr_cell.row,
                                       dict_->WildcardID())) {
      // rat_subtr contains ratings sum of the two adjacent blobs to be merged.
      // rat_subtr will be subtracted from the ratings sum of the path, since
      // the blobs will be joined into a new blob, whose rating is yet unknown.
      float rat_subtr = curr_b->rating() + parent_vse->curr_b->rating();
      // ol_subtr contains the outline length of the blobs that will be joined.
      float ol_subtr =
          AssociateUtils::ComputeOutlineLength(rating_cert_scale, *curr_b) +
          AssociateUtils::ComputeOutlineLength(rating_cert_scale,
                                               *(parent_vse->curr_b));
      // ol_dif is the outline of the path without the two blobs to be joined.
      float ol_dif = vse->outline_length - ol_subtr;
      // priority is set to the average rating of the path per unit of outline,
      // not counting the ratings of the pieces to be joined.
      float priority = ol_dif > 0 ? (vse->ratings_sum-rat_subtr)/ol_dif : 0.0;
      GeneratePainPoint(pain_coord.col, pain_coord.row, LM_PPTYPE_PATH,
                        priority, true, max_char_wh_ratio_, word_res);
    } else if (debug_level_ > 3) {
      tprintf("NO pain point (Classified) for col=%d row=%d type=%s\n",
              pain_coord.col, pain_coord.row,
              LMPainPointsTypeName[LM_PPTYPE_PATH]);
      BLOB_CHOICE_IT b_it(word_res->ratings->get(pain_coord.col,
                                                 pain_coord.row));
      for (b_it.mark_cycle_pt(); !b_it.cycled_list(); b_it.forward()) {
        BLOB_CHOICE* choice = b_it.data();
        choice->print_full();
      }
    }

    curr_vse = parent_vse;
    curr_b = curr_vse->curr_b;
  }
}