def from_config_file(config_file):
c = Config()
config = configparser.ConfigParser()
config.read(config_file)
for k, v in config['mask_rcnn'].items():
setattr(c, k.upper(), literal_eval(v))
c.__init__()
return c
def __init__(self, channel = 3):
assert channel == 1 or channel == 3 or channel == 4, "The channel must be 1, 3 or 4! Given: {}".format(channel)
self.IMAGE_CHANNEL_COUNT = channel
if channel == 1 or channel == 3: return
elif channel == 4:
self.MEAN_PIXEL = np.append(self.MEAN_PIXEL, 10)
#elif channel == 1:
#self.MEAN_PIXEL = [np.sum(self.MEAN_PIXEL) / 3]
Config.__init__(self)
def __init__(self, dataset_name, classnames):
# Give the configuration a recognizable name
self.dataset_name = dataset_name
self.NAME = dataset_name
self.CLASS_NAMES = classnames
self.ALL_CLASS_NAMES = ['BG'] + self.CLASS_NAMES
# Number of classes (including background)
self.NUM_CLASSES = len(self.ALL_CLASS_NAMES)
self.map_name_to_id = {}
Config.__init__(self)
def load_network(self): config = Config() config.NAME = 'predict' config.NUM_CLASSES = 1 + 1 config.IMAGES_PER_GPU = 1 config.GPU_COUNT = 1 additional_info = json.load(open(self.config_path)) for i,j in additional_info.items(): try: setattr(config,i,eval(j)) except: setattr(config,i,j) config.__init__() from mrcnn import model as modellib self.model = modellib.MaskRCNN(mode="inference", model_dir='./',config=config)
def __init__(self, taxonomy):
# Number of classes (including background)
self.NUM_CLASSES = 1 + len(taxonomy) # Background + objects
Config.__init__(self) # run __init__ from Config
def __init__(self,num_class):
Config.__init__(self,num_class)
def __init__(self, class_names):
self.NUM_CLASSES = len(class_names)
Config.__init__(self)
def __init__(self, batch_size):
Config.__init__(self)
self.BATCH_SIZE = batch_size
