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, url): self.log = logging.getLogger('%s.%s' % (__name__, self.__class__.__name__)) self._managedObjects = {} self.session = None # create a connection and connect to qpidd # TODO: (redmine 277) - Make this use actual amqp:// urls... currently, only host works self.connection = cqpid.Connection(url, "{reconnect:True}") self.connection.open() # Create, configure, and open a QMFv2 agent session using the connection. self.session = AgentSession(self.connection) self.session.setVendor("redhat.com") self.session.setProduct("imagefactory") self.session.open() # Initialize the parent class with the session. AgentHandler.__init__(self, self.session) # Register our schemata with the agent session. self.session.registerSchema(ImageFactory.qmf_schema) self.session.registerSchema(BuildAdaptor.qmf_schema) self.session.registerSchema(BuildAdaptor.qmf_event_schema_status) self.session.registerSchema(BuildAdaptor.qmf_event_schema_percentage) self.session.registerSchema(BuildAdaptor.qmf_event_schema_build_failed) # Now add the image factory object self.image_factory = ImageFactory() self.image_factory.agent = self self.image_factory_addr = self.session.addData( self.image_factory.qmf_object, "image_factory") self.log.info("image_factory has qmf/qpid address: %s", self.image_factory_addr)
def resize_image_in_folder(path): if os.path.isfile(path): if is_deal(path): return; imageFactory = ImageFactory(""); try: imageFactory.getThumbByCut(232, 93, path, "_232x93"); imageFactory.getThumbByCut(80, 80, path, "_80x80"); except: pass; elif os.path.isdir(path): files = os.listdir(path); for imageFile in files: resize_image_in_folder(path + "/" + imageFile); else: print("the given path is not a folder or a file :%s"%path);
def getContentBoxesWorker(socketID, filename): # instantiate a publisher (send to nodejs server) context = zmq.Context() socket = context.socket(zmq.PUB) socket.bind("tcp://127.0.0.1:5556") # msg["socketID"] is clientID, msg["filename"] is path Im = ImageFactory() path = "../public/" + filename Im.initialize(path) # initialize class_list Im.class_list = np.zeros((len(Im.feature_list), 2)) for x in range(0, len(Im.feature_list)): Im.class_list[x][0] = 1.0 # create JSON string before ... json_string = '{"socketID":"' + socketID + '", "contentBoxes" :' + tojson([Im.content_list, Im.class_list]) + "}" socket.send_string(json_string) pass
def run(): prepare_directories() download_file() image_factory = ImageFactory(resize=256, crop=224) print("Formatting train set") format_set("train", TRAIN_FILE, image_factory) print("Formatting val set") format_set("val", VAL_FILE, image_factory)
def getContentBoxesWorker(socketID, filename): # instantiate a publisher (send to nodejs server) context = zmq.Context() socket = context.socket(zmq.PUB) socket.bind("tcp://127.0.0.1:5556") # msg["socketID"] is clientID, msg["filename"] is path Im = ImageFactory() path = "../public/" + filename Im.initialize(path) # initialize class_list Im.class_list = np.zeros((len(Im.feature_list), 2)) for x in range(0, len(Im.feature_list)): Im.class_list[x][0] = 1.0 # create JSON string before ... json_string = '{"socketID":"' + socketID + '", "contentBoxes" :' + tojson( [Im.content_list, Im.class_list]) + '}' socket.send_string(json_string) pass