def _find_group_unions(self, G, evaluate_function, max_parts_per_group=5,
max_graph_size=16, criterion="min"):
import image_utilities
progress = util.ProgressFactory("Grouping glyphs...", G.nsubgraphs)
try:
found_unions = []
for root in G.get_subgraph_roots():
if G.size_of_subgraph(root) > max_graph_size:
continue
best_grouping = G.optimize_partitions(
root, evaluate_function, max_parts_per_group, max_graph_size, criterion)
if not best_grouping is None:
for subgroup in best_grouping:
if len(subgroup) > 1:
union = image_utilities.union_images(subgroup)
found_unions.append(union)
classification, confidence = self.guess_glyph_automatic(union)
union.classify_heuristic(classification)
part_name = "_group._part." + classification[0][1]
for glyph in subgroup:
glyph.classify_heuristic(part_name)
progress.step()
finally:
progress.kill()
return found_unions
def _find_group_unions(self, G, evaluate_function, max_parts_per_group=5,
max_graph_size=16, criterion="min"):
import image_utilities
progress = util.ProgressFactory("Grouping glyphs...", G.nsubgraphs)
try:
found_unions = []
for root in G.get_subgraph_roots():
if G.size_of_subgraph(root) > max_graph_size:
continue
best_grouping = G.optimize_partitions(
root, evaluate_function, max_parts_per_group, max_graph_size, criterion)
if not best_grouping is None:
for subgroup in best_grouping:
if len(subgroup) > 1:
union = image_utilities.union_images(subgroup)
found_unions.append(union)
classification, confidence = self.guess_glyph_automatic(union)
union.classify_heuristic(classification)
part_name = "_group._part." + classification[0][1]
for glyph in subgroup:
glyph.classify_heuristic(part_name)
progress.step()
finally:
progress.kill()
return found_unions
def classify_list_manual(self, glyphs, id):
"""**classify_list_manual** (ImageList *glyphs*, String *id*)
Sets the classification of the given *glyphs* to the given *id* and
then adds the glyphs to the training data. Call this function when the
end user definitively knows the identity of the glyphs.
If *id* begins with the special prefix ``_group``, all of the glyphs
in *glyphs* are combined and the result is added to the training
data. This is useful for characters that always appear with multiple
connnected components, such as the lower-case *i*.
*glyphs*
The glyphs to classify.
*id*
The class name.
.. note::
Here *id* is a simple string, not of the `id_name`_ format, since
the confidence of a manual classification is always 1.0."""
if id.startswith('_group'):
if len(glyphs) > 1:
import image_utilities
parts = id.split('.')
sub = '.'.join(parts[1:])
union = image_utilities.union_images(glyphs)
for glyph in glyphs:
if glyph.nrows > 2 and glyph.ncols > 2:
glyph.classify_heuristic('_group._part.' + sub)
self.generate_features(glyph)
added, removed = self.classify_glyph_manual(union, sub)
added.append(union)
return added, removed
else:
# grouping a single glyph corrupts the classifier_glyph.xml file
raise ClassifierError(
"Grouping of only a single glyph is not allowed.")
added = []
removed = util.sets.Set()
for glyph in glyphs:
for child in glyph.children_images:
removed.add(child)
new_glyphs = []
for glyph in glyphs:
# Don't re-insert removed children glyphs
if not glyph in removed:
if not glyph in self.database:
self.generate_features(glyph)
new_glyphs.append(glyph)
glyph.classify_manual([(1.0, id)])
added.extend(self._do_splits(self, glyph))
self.database.extend(new_glyphs)
return added, list(removed)
def classify_list_manual(self, glyphs, id):
"""**classify_list_manual** (ImageList *glyphs*, String *id*)
Sets the classification of the given *glyphs* to the given *id* and
then adds the glyphs to the training data. Call this function when the
end user definitively knows the identity of the glyphs.
If *id* begins with the special prefix ``_group``, all of the glyphs
in *glyphs* are combined and the result is added to the training
data. This is useful for characters that always appear with multiple
connnected components, such as the lower-case *i*.
*glyphs*
The glyphs to classify.
*id*
The class name.
.. note::
Here *id* is a simple string, not of the `id_name`_ format, since
the confidence of a manual classification is always 1.0."""
if id.startswith('_group'):
if len(glyphs) > 1:
import image_utilities
parts = id.split('.')
sub = '.'.join(parts[1:])
union = image_utilities.union_images(glyphs)
for glyph in glyphs:
if glyph.nrows > 2 and glyph.ncols > 2:
glyph.classify_heuristic('_group._part.' + sub)
self.generate_features(glyph)
added, removed = self.classify_glyph_manual(union, sub)
added.append(union)
return added, removed
else:
# grouping a single glyph corrupts the classifier_glyph.xml file
raise ClassifierError("Grouping of only a single glyph is not allowed.")
added = []
removed = util.sets.Set()
for glyph in glyphs:
for child in glyph.children_images:
removed.add(child)
new_glyphs = []
for glyph in glyphs:
# Don't re-insert removed children glyphs
if not glyph in removed:
if not glyph in self.database:
self.generate_features(glyph)
new_glyphs.append(glyph)
glyph.classify_manual([(1.0, id)])
added.extend(self._do_splits(self, glyph))
self.database.extend(new_glyphs)
return added, list(removed)
def _evaluate_subgroup(self, subgroup):
import image_utilities
if len(subgroup) > 1:
union = image_utilities.union_images(subgroup)
classification, confidence = self.guess_glyph_automatic(union)
classification_name = classification[0][1]
if (classification_name.startswith("_split") or
classification_name.startswith("skip")):
return 0.0
else:
return classification[0][0]
if len(subgroup):
classification = subgroup[0].id_name[0]
if classification[1].startswith('_group._part'):
return 0.0
return classification[0]
raise ValueError("Something is wrong here... Either you don't have classifier data or there is an internal error in the grouping algorithm.")
def _evaluate_subgroup(self, subgroup):
import image_utilities
if len(subgroup) > 1:
union = image_utilities.union_images(subgroup)
classification, confidence = self.guess_glyph_automatic(union)
classification_name = classification[0][1]
if (classification_name.startswith("_split") or
classification_name.startswith("skip")):
return 0.0
else:
return classification[0][0]
if len(subgroup):
classification = subgroup[0].id_name[0]
if classification[1].startswith('_group._part'):
return 0.0
return classification[0]
raise ValueError("Something is wrong here... Either you don't have classifier data or there is an internal error in the grouping algorithm.")