def __init__(self,
model,
grid_size,
logger=LogFactory.get_default_logger()):
self.model = model
self.grid_size = grid_size
self.logger = logger
def __init__(self,
model,
neighbour_area,
logger=LogFactory.get_default_logger()):
self.model = model
self.neighbour_area = neighbour_area
self.probability_calculator = CachedProbabilityCalculator()
self.logger = logger
def train_model(train_config=TextureTrainConfiguration(), logger=LogFactory.get_default_logger()):
"""
Gets examples path from config, trains a svm then dumps the model in a file
"""
logger.log("Started training texture model")
trainer = TextureModelTrainer(
train_config.path_pos, train_config.path_neg, train_config.skin_label, train_config.non_skin_label)
trainer.__train_and_store_model(train_config.path_models + "/" + train_config.selected_classifier, logger)
logger.log("Done")
def __init__(self,
with_position,
sigma,
tau,
logger=LogFactory.get_default_logger()):
self.with_position = with_position
self.sigma = sigma
self.tau = tau
self.logger = logger
def get_detector(model_path,
detection_type,
detection_window_size,
logger=LogFactory.get_default_logger()):
serializator = SerializationUtils(logger)
model = serializator.load_joblib_object(model_path)
if detection_type == 0:
return GridDetector(model, detection_window_size, logger)
else:
return PerPixelDetector(model, detection_window_size, logger)
def get_detector(model_path, detector_type, neighbour_area, logger=LogFactory.get_default_logger()):
serializer = SerializationUtils(logger)
model = serializer.load_object(model_path)
if detector_type == 0:
detector = SimpleDetector(model, logger)
elif detector_type == 1:
detector = NeighbourDetector(model, neighbour_area, logger)
else:
detector = AverageOnSuperpixelDetector(model, neighbour_area, logger)
return detector
def __init__(self,
path_pos,
path_neg,
skin_label,
non_skin_label,
logger=LogFactory.get_default_logger()):
self.path_pos = path_pos
self.path_neg = path_neg
self.skin_label = skin_label
self.non_skin_label = non_skin_label
self.model = None
self.logger = logger
def __init__(self,
path_train,
color_space,
logger=LogFactory.get_default_logger()):
self.path_train = path_train
self.color_space = color_space
self.appearances = {}
self.appearances_as_skin = {}
self.skin_pixels = 0
self.non_skin_pixels = 0
self.logger = logger
def __init__(self,
path_positive_images,
path_negative_images,
color_space,
logger=LogFactory.get_default_logger()):
self.path_positive_images = path_positive_images
self.path_negative_images = path_negative_images
self.color_space = color_space
self.appearances = {}
self.appearances_as_skin = {}
self.skin_pixels = 0
self.non_skin_pixels = 0
self.logger = logger
def __init__(self,
config=RunConfiguration(),
logger=LogFactory.get_default_logger()):
logger.log('Initializing evaluator')
self.config = config
self.quickshift = QuickshiftSegmentation(config.qs_with_position,
config.qs_sigma,
config.qs_tau, logger)
self.spm_detector = SpmDetectorFactory.get_detector(
config.spm_model_path, config.spm_type, config.spm_neighbour_area,
logger)
self.texture_detector = TextureDetectorFactory.get_detector(
config.texture_model_path, config.texture_detection_type,
config.texture_detection_area, logger)
self.logger = logger
def train_spm_model(train_config=SpmTrainConfiguration(),
logger=LogFactory.get_default_logger()):
"""
Default factory method ; uses values from config
"""
logger.log("Started training model")
if train_config.database == 'compaq':
extractor = CompaqComponentExtractor(train_config.path_compaq,
train_config.color_space,
logger)
else:
extractor = SfaComponentExtractor(train_config.path_pos,
train_config.path_neg,
train_config.color_space, logger)
trainer = SPMModelTrainer(extractor, train_config.color_space)
trainer.__train_and_store_model(train_config.path_models + '/' +
train_config.selected_model)
logger.log("Done")
def __init__(self, model, window_size, logger=LogFactory.get_default_logger()):
self.model = model
self.window_size = window_size
self.probability_calculator = CachedProbabilityCalculator()
self.logger = logger
def __init__(self, logger=LogFactory.get_default_logger()):
self.logger = logger
def __init__(self, model, logger=LogFactory.get_default_logger()):
self.model = model
self.logger = logger