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 classify_heuristic(self, id_name):
"""Classifies the image as the value *id_name* and sets the state
to HEURISTIC. Use this method when a heuristic process has classified
this glyph. This method also unsets the *confidence* map.
*id_name*
Can come in one of two forms:
**string**
image is classified using the given ``.``-delimited class name.
As in this case no confidence is provided, it is set to 0.5.
**list of tuples**
A list of tuples where each tuple is the pair (*confidence*, *class_name*).
*confidence*
A value in range (0, 1), where 0 is uncertain and 1 is certain.
*class_name*
A ``.``-delimited class name."""
if util.is_string_or_unicode(id_name):
id_name = [(0.5, id_name)]
elif type(id_name) != ListType:
raise TypeError("id_name must be a string or a list")
self.id_name = id_name
self.confidence = {}
self.classification_state = HEURISTIC
def classify_automatic(self, id_name):
"""Classifies the image as the value *id_name* and sets the state
to AUTOMATIC. Use this method when an automatic classifier has classified
this glyph. Note that this method does not touch the *confidence* map for
the main id; this must be set separately.
*id_name*
Can come in one of two forms:
**string**
image is classified using the given ``.``-delimited class name.
As in this case no confidence is provided, it is set to zero.
**list of tuples**
A list of tuples where each tuple is the pair (*confidence*, *class_name*).
*confidence*
A value in range (0, 1), where 0 is uncertain and 1 is certain.
*class_name*
A ``.``-delimited class name."""
if util.is_string_or_unicode(id_name):
id_name = [(0.0, id_name)]
elif type(id_name) != ListType:
raise TypeError("id_name must be a string or a list")
self.id_name = id_name
self.classification_state = AUTOMATIC
def __init__(self, name=None, default="", extension="*.*"):
Arg.__init__(self, name)
if not util.is_string_or_unicode(default):
raise TypeError("'default' must be a string")
self.default = default
## if not util.is_string_or_unicode(extension):
## raise TypeError("'extension' must be a string")
self.extension = extension
def __init__(self, name=None, default="", extension="*.*"):
Arg.__init__(self, name)
if not util.is_string_or_unicode(default):
raise TypeError("'default' must be a string")
self.default = default
## if not util.is_string_or_unicode(extension):
## raise TypeError("'extension' must be a string")
self.extension = extension
def __init__(self, name=None, default=None):
Arg.__init__(self, name)
if default is None:
self.has_default = False
self.default = ''
else:
self.has_default = True
self.default = default
if not util.is_string_or_unicode(self.default):
raise TypeError("'default' must be an int")
def __init__(self, name=None, default=None):
Arg.__init__(self, name)
if default is None:
self.has_default = False
self.default = ''
else:
self.has_default = True
self.default = default
if not util.is_string_or_unicode(self.default):
raise TypeError("'default' must be an int")
def __init__(self, name=None, check_box='', default=None, enabled=True):
Arg.__init__(self, name)
if not util.is_string_or_unicode(check_box):
raise TypeError("'check_box' must be a string")
self.check_box = check_box
if default is None:
self.has_default = False
self.default = False
else:
self.has_default = True
self.default = default
self.default = bool(self.default)
self.enabled = bool(enabled)
def __init__(self, name=None, check_box='', default=None, enabled=True):
Arg.__init__(self, name)
if not util.is_string_or_unicode(check_box):
raise TypeError("'check_box' must be a string")
self.check_box = check_box
if default is None:
self.has_default = False
self.default = False
else:
self.has_default = True
self.default = default
self.default = bool(self.default)
self.enabled = bool(enabled)
def __init__(self, name):
self.name = name
if name is not None and not util.is_string_or_unicode(name):
raise TypeError("'name' must be a string")
self.has_default = False
def __init__(self, name):
self.name = name
if name is not None and not util.is_string_or_unicode(name):
raise TypeError("'name' must be a string")
self.has_default = False