def remove_from_database(self, glyphs):
"""**remove_from_database** (ImageList *glyphs*)
Removes the given glyphs from the classifier training data. Ignores silently
if a given glyph is not in the training data.
"""
glyphs = util.make_sequence(glyphs)
for glyph in glyphs:
if glyph in self.database:
self.database.remove(glyph)
def remove_from_database(self, glyphs):
"""**remove_from_database** (ImageList *glyphs*)
Removes the given glyphs from the classifier training data. Ignores silently
if a given glyph is not in the training data.
"""
glyphs = util.make_sequence(glyphs)
for glyph in glyphs:
if glyph in self.database:
self.database.remove(glyph)
def get_feature_functions(cls, features="all"):
from gamera import plugin
global all_features
if all_features is None:
all_features = plugin.methods_flat_category("Features", ONEBIT)
all_features.sort()
if features == "all" or features is None:
functions = all_features
return functions, cls._get_feature_vector_size(functions)
features = util.make_sequence(features)
all_strings = True
for feature in features:
if not util.is_string_or_unicode(feature):
all_strings = False
break
if not all_strings:
import plugin
all_functions = False
if (
type(features) == tuple
and len(features) == 2
and type(features[0]) == list
and type(features[1]) == int
):
all_functions = True
for feature in features[0]:
if not (
type(feature) == tuple
and util.is_string_or_unicode(feature[0])
and issubclass(feature[1], plugin.PluginFunction)
):
all_functions = False
break
if not all_functions:
raise ValueError("'%s' is not a valid way to specify a list of features." % str(features))
else:
return features
else:
features.sort()
functions = []
for feature in features:
found = 0
for i in all_features:
if feature == i[0]:
functions.append(i)
found = 1
break
if not found:
raise ValueError("'%s' is not a known feature function." % feature)
functions.sort()
return functions, cls._get_feature_vector_size(functions)
def add_to_database(self, glyphs):
"""**add_to_database** (ImageList *glyphs*)
Adds the given glyph (or list of glyphs) to the classifier training data. Will not add duplicates
to the training data. Unlike classify_glyph_manual_, no grouping support is performed.
"""
glyphs = util.make_sequence(glyphs)
new_glyphs = []
for glyph in glyphs:
if (glyph.classification_state == core.MANUAL
and not glyph in self.database):
self.generate_features(glyph)
new_glyphs.append(glyph)
self.database.extend(new_glyphs)
def add_to_database(self, glyphs):
"""**add_to_database** (ImageList *glyphs*)
Adds the given glyph (or list of glyphs) to the classifier training data. Will not add duplicates
to the training data. Unlike classify_glyph_manual_, no grouping support is performed.
"""
glyphs = util.make_sequence(glyphs)
new_glyphs = []
for glyph in glyphs:
if (glyph.classification_state == core.MANUAL and
not glyph in self.database):
self.generate_features(glyph)
new_glyphs.append(glyph)
self.database.extend(new_glyphs)
def __init__(self, glyphs, max_width=100, max_height=100):
"""Creates a grid index to store the given set of glyphs. Note that
the init function only creates a grid big enough to hold the glyphs,
it does not actually store them... That must be done by calling
GridIndex.add_glyph. max_width and max_height are the maximum size
(in pixels) of each cell."""
glyphs = util.make_sequence(glyphs)
if len(glyphs) == 0:
raise ValueError(
"GridIndex must be initialised with at least one glyph")
self.grid_rect = glyphs[0].union_rects(glyphs)
self.cell_width = int(max_width)
self.cell_height = int(max_height)
self.cell_ncols = int(self.grid_rect.width / self.cell_width) + 1
self.cell_nrows = int(self.grid_rect.height / self.cell_height) + 1
self._create_cells()
def get_feature_functions(cls, features='all'):
global all_features
if all_features is None:
all_features = plugin.methods_flat_category('Features', ONEBIT)
all_features.sort()
if features == 'all' or features is None:
functions = all_features
return functions, cls._get_feature_vector_size(functions)
features = util.make_sequence(features)
all_strings = True
for feature in features:
if not util.is_string_or_unicode(feature):
all_strings = False
break
if not all_strings:
all_functions = False
if (type(features) == tuple and len(features) == 2
and type(features[0]) == list
and type(features[1]) == int):
all_functions = True
for feature in features[0]:
if not (type(feature) == tuple
and util.is_string_or_unicode(feature[0])
and issubclass(feature[1], plugin.PluginFunction)):
all_functions = False
break
if not all_functions:
raise ValueError(
"'%s' is not a valid way to specify a list of features." %
str(features))
else:
return features
else:
features.sort()
functions = []
for feature in features:
found = 0
for i in all_features:
if feature == i[0]:
functions.append(i)
found = 1
break
if not found:
raise ValueError("'%s' is not a known feature function." %
feature)
functions.sort()
return functions, cls._get_feature_vector_size(functions)
def merge_glyphs(self, glyphs):
glyphs = util.make_sequence(glyphs)
self.generate_features_on_glyphs(glyphs)
self.database.extend(glyphs)
def set_glyphs(self, glyphs):
glyphs = util.make_sequence(glyphs)
self.clear_glyphs()
self.generate_features_on_glyphs(glyphs)
self.database.extend(glyphs)
def __init__(self, list=[], name="Arguments", function=None, title=None):
self.list = util.make_sequence(list)
self.valid = 1
self.name = name
self.function = function
self.title = title
def merge_glyphs(self, glyphs):
glyphs = util.make_sequence(glyphs)
self.generate_features_on_glyphs(glyphs)
self.database.extend(glyphs)
def set_glyphs(self, glyphs):
glyphs = util.make_sequence(glyphs)
self.clear_glyphs()
self.generate_features_on_glyphs(glyphs)
self.database.extend(glyphs)
def __init__(self, list=[], name="Arguments", function=None, title=None):
self.list = util.make_sequence(list)
self.valid = 1
self.name = name
self.function = function
self.title = title
