BLOB_CHOICE_LIST *Wordrec::classify_piece(TBLOB *pieces,
const DENORM& denorm,
SEAMS seams,
inT16 start,
inT16 end,
BlamerBundle *blamer_bundle) {
BLOB_CHOICE_LIST *choices;
TBLOB *blob;
inT16 x;
join_pieces(pieces, seams, start, end);
for (blob = pieces, x = 0; x < start; x++) {
blob = blob->next;
}
choices = classify_blob(blob, denorm, "pieces:", White, blamer_bundle);
break_pieces(blob, seams, start, end);
#ifndef GRAPHICS_DISABLED
if (wordrec_display_segmentations > 2) {
STATE current_state;
SEARCH_STATE chunk_groups;
set_n_ones (¤t_state, array_count(seams));
chunk_groups = bin_to_chunks(¤t_state, array_count(seams));
display_segmentation(pieces, chunk_groups);
window_wait(segm_window);
memfree(chunk_groups);
}
#endif
return (choices);
}
BLOB_CHOICE_LIST *Wordrec::classify_piece(TBLOB *pieces,
SEAMS seams,
inT16 start,
inT16 end) {
STATE current_state;
BLOB_CHOICE_LIST *choices;
TBLOB *pblob;
TBLOB *blob;
TBLOB *nblob;
inT16 x;
SEARCH_STATE chunk_groups;
set_n_ones (¤t_state, array_count (seams));
join_pieces(pieces, seams, start, end);
for (blob = pieces, pblob = NULL, x = 0; x < start; x++) {
pblob = blob;
blob = blob->next;
}
for (nblob = blob->next; x < end; x++)
nblob = nblob->next;
choices = classify_blob (pblob, blob, nblob, NULL, "pieces:", White);
break_pieces(blob, seams, start, end);
#ifndef GRAPHICS_DISABLED
if (wordrec_display_segmentations > 2) {
chunk_groups = bin_to_chunks (¤t_state, array_count (seams));
display_segmentation(pieces, chunk_groups);
window_wait(segm_window);
memfree(chunk_groups);
}
#endif
return (choices);
}
BLOB_CHOICE_LIST *Wordrec::classify_piece(const GenericVector<SEAM*>& seams,
inT16 start,
inT16 end,
const char* description,
TWERD *word,
BlamerBundle *blamer_bundle) {
if (end > start) join_pieces(seams, start, end, word);
BLOB_CHOICE_LIST *choices = classify_blob(word->blobs[start], description,
White, blamer_bundle);
// Set the matrix_cell_ entries in all the BLOB_CHOICES.
BLOB_CHOICE_IT bc_it(choices);
for (bc_it.mark_cycle_pt(); !bc_it.cycled_list(); bc_it.forward()) {
bc_it.data()->set_matrix_cell(start, end);
}
if (end > start) break_pieces(seams, start, end, word);
return (choices);
}
// Creates a joined copy of the blobs between x and y (inclusive) and
// inserts as the first blob at word->rebuild_word->blobs.
// Returns a deep copy of the classifier results for the blob.
BLOB_CHOICE_LIST *Wordrec::join_blobs_and_classify(
WERD_RES* word, int x, int y, int choice_index, MATRIX *ratings,
BLOB_CHOICE_LIST_VECTOR *old_choices) {
// Join parts to make the blob if needed.
if (x != y)
join_pieces(word->chopped_word->blobs, word->seam_array, x, y);
TBLOB *blob = word->chopped_word->blobs;
for (int i = 0; i < x; i++) {
blob = blob->next;
}
// Deep copy this blob into the output word.
TBLOB* copy_blob = new TBLOB(*blob);
copy_blob->next = word->rebuild_word->blobs;
word->rebuild_word->blobs = copy_blob;
BLOB_CHOICE_LIST *choices = NULL;
// First check to see if we can look up the classificaiton
// in old_choices (if there is no need to merge blobs).
if (choice_index >= 0 && old_choices != NULL) {
choices = old_choices->get(choice_index);
old_choices->set(NULL, choice_index);
}
// The ratings matrix filled in by the associator will contain the next most
// up-to-date classification info. Thus we look up the classification there
// next, and only call classify_blob() if the classification is not found.
if (choices == NULL && ratings != NULL) {
choices = ratings->get(x, y);
if (choices != NOT_CLASSIFIED) {
ratings->put(x, y, NULL);
}
}
// Get the choices for the blob by classification if necessary.
if (choices == NULL) {
choices = classify_blob(blob, word->denorm, "rebuild", Orange,
word->blamer_bundle);
}
// Undo join_pieces to restore the chopped word to its fully chopped state.
if (x != y)
break_pieces(blob, word->seam_array, x, y);
return choices;
}
/**********************************************************************
* classify_piece
*
* Create a larger piece from a collection of smaller ones. Classify
* it and return the results. Take the large piece apart to leave
* the collection of small pieces un modified.
**********************************************************************/
CHOICES classify_piece(TBLOB *pieces,
SEAMS seams,
INT16 start,
INT16 end,
INT32 fx,
STATE *this_state,
STATE *best_state,
INT32 pass,
INT32 blob_index) {
STATE current_state;
CHOICES choices;
TBLOB *pblob;
TBLOB *blob;
TBLOB *nblob;
INT16 x;
SEARCH_STATE chunk_groups;
set_n_ones (¤t_state, array_count (seams));
join_pieces(pieces, seams, start, end);
for (blob = pieces, pblob = NULL, x = 0; x < start; x++) {
pblob = blob;
blob = blob->next;
}
for (nblob = blob->next; x < end; x++)
nblob = nblob->next;
choices = classify_blob (pblob, blob, nblob, NULL, fx, "pieces:", White,
this_state, best_state, pass, blob_index);
break_pieces(blob, seams, start, end);
#ifndef GRAPHICS_DISABLED
if (display_segmentations > 2) {
chunk_groups = bin_to_chunks (¤t_state, array_count (seams));
display_segmentation(pieces, chunk_groups);
window_wait(segm_window);
memfree(chunk_groups);
}
#endif
return (choices);
}
