def __init__(self, uid=None, pid=None, exit_tme=None, tme=None, int_in_volume=0, int_in_rate=0, int_out_volume=0, int_out_rate=0, ext_in_volume=0, ext_in_rate=0, ext_out_volume=0, ext_out_rate=0): Feature.__init__(pid=pid, uid=uid, tme=tme) self.exit_tme = self._convert_to_default_type("exit_tme", exit_tme) self.int_in_volume = self._convert_to_default_type( "int_in_volume", int_in_volume) self.int_in_rate = self._convert_to_default_type( "int_in_rate", int_in_rate) self.int_out_volume = self._convert_to_default_type( "int_out_volume", int_out_volume) self.int_out_rate = self._convert_to_default_type( "int_out_rate", int_out_rate) self.ext_in_volume = self._convert_to_default_type( "ext_in_volume", ext_in_volume) self.ext_in_rate = self._convert_to_default_type( "ext_in_rate", ext_in_rate) self.ext_out_volume = self._convert_to_default_type( "ext_out_volume", ext_out_volume) self.ext_out_rate = self._convert_to_default_type( "ext_out_rate", ext_out_rate)
def __init__(self,**kwargs): u""" :param framefilter: selected framenumber where we will compute histogram :type: array """ Feature.__init__(self,**kwargs)
def __init__(self, biodb, step_size=5, levels=[], name_hier=[]): Feature.__init__(self, biodb= biodb) self.step_size= step_size if levels == []: levels=[None]*3 self.levels= levels if name_hier == []: name_hier= [""]*3 self.name_hier= name_hier self.links=[]
def __init__(self, biodb, step_size=5, levels=[], name_hier=[], parent_hier=[]): Feature.__init__(self, biodb= biodb) self.step_size= step_size if parent_hier == []: parent_hier=[None]*3 self.parent_hier= parent_hier if levels == []: levels=[None]*3 self.levels= levels self.links=[]
def __init__(self, quantityName, zScale=1.0, offset=0.0, **kwargs): ''' Parameters: quantityName: string - name of a quantity zScale: float - multiply point z-values by this offset: float - add this to point z-values ''' Feature.__init__(self, **kwargs) self.quantityName = quantityName self.zScale = zScale self.offset = offset
def __init__(self, field, word_file): """ Get the word list from the specified file. :param field: The field to which this feature belongs. :param word_file: The path to an alphabetized word list, one per line. :return: None """ word_file = field.settings.resolve_path(word_file) with open(word_file, 'r') as f: words = f.readlines() words = [w.strip() for w in words if w.islower()] self._dict_words = words Feature.__init__(self, field)
def __init__(self, _id=0): Feature.__init__(self) self.id = _id # Edge_ID(用于显示) self.starting_node_id = -1 self.ending_node_id = -1
def __init__(self): Feature.__init__(self) self.background_image = np.array([]) self.video_capture = cv2.VideoCapture()
def __init__(self,**kwargs): u""" compute video signature based on dct descriptor """ Feature.__init__(self,**kwargs)
def __init__(self,**kwargs): Feature.__init__(self,**kwargs) self.log = logging.getLogger('pimpy.image.features.sift')
def __init__(self, field, string): self._string = string Feature.__init__(self, field)
def __init__(self, field, reverse=False): self.reverse = reverse Feature.__init__(self, field)
def __init__(self, field, phrases): self._phrases = phrases Feature.__init__(self, field)
def __init__(self,**kwargs): Feature.__init__(self,**kwargs) self.log = logging.getLogger('pimpy.image.features.goodfeaturestotrack')
def __init__(self,**kwargs): u""" compute video gist for each frame """ Feature.__init__(self,**kwargs)
def __init__(self, column): Feature.__init__(self) self.column = column