class CorePy(object):
def __init__(self, path, predictorType):
super(CorePy, self).__init__()
self.image = ImageFactory()
self.path = path
if predictorType == "kppv":
self.predictor = Kppv()
# elif predictorType == "mlp":
# self.predictor = Mlp()
else:
self.predictor = None
self.max_distance = 0
def setImage(self, path_to_image):
self.image.initialize(path_to_image)
def predict_current(self):
predicted_classes, result = np.zeros(
(len(self.image.feature_list), 2)), 0
for x in range(0, len(self.image.feature_list)):
predicted_classes[x], distance = self.predictor.predict(
self.image.feature_list[x])
result += predicted_classes[x]
if distance >= 0:
self.max_distance = max(self.max_distance, distance)
self.image.class_list = predicted_classes
pass
def train_predictor(self):
self.predictor.train(self.image.feature_list, self.image.class_list)
class CorePy(object):
def __init__(self, path, predictorType):
super(CorePy, self).__init__()
self.image = ImageFactory()
self.path = path
if predictorType == "kppv":
self.predictor = Kppv()
# elif predictorType == "mlp":
# self.predictor = Mlp()
else:
self.predictor = None
self.max_distance = 0
def setImage(self, path_to_image):
self.image.initialize(path_to_image)
def predict_current(self):
predicted_classes, result = np.zeros((len(self.image.feature_list), 2)), 0
for x in range(0,len(self.image.feature_list)):
predicted_classes[x], distance = self.predictor.predict(self.image.feature_list[x])
result += predicted_classes[x]
if distance >= 0:
self.max_distance = max(self.max_distance, distance)
self.image.class_list = predicted_classes
pass
def train_predictor(self):
self.predictor.train(self.image.feature_list, self.image.class_list)
def __init__(self, path, predictorType):
super(CorePy, self).__init__()
self.image = ImageFactory()
self.path = path
if predictorType == "kppv":
self.predictor = Kppv()
# elif predictorType == "mlp":
# self.predictor = Mlp()
else:
self.predictor = None
self.max_distance = 0
def __init__(self, path, predictorType):
super(CorePy, self).__init__()
self.image = ImageFactory()
self.path = path
if predictorType == "kppv":
self.predictor = Kppv()
# elif predictorType == "mlp":
# self.predictor = Mlp()
else:
self.predictor = None
self.max_distance = 0
