def breed(self, mother, father): """Make two children as parts of their parents. Args: mother (dict): Network parameters father (dict): Network parameters Returns: (list): Two network objects """ children = [] for _ in range(2): child = {} # Loop through the parameters and pick params for the kid. for param in self.nn_param_choices: child[param] = random.choice( [mother.network[param], father.network[param]]) # Now create a network object. network = Network(self.nn_param_choices) network.create_set(child) # Randomly mutate some of the children. if self.mutate_chance > random.random(): network = self.mutate(network) children.append(network) return children
def run(): nodes = [Node('0', 0, 0), Node('1', 0, 50), Node('2', 0, 400)] edges = [Edge(nodes[0], nodes[1]), Edge(nodes[1], nodes[2])] newNet = Network(nodes, edges) with open("local\\sumo.path", 'r') as f: sumo_path = f.read() sumo_tools = sumo_path + '\\tools' sumo_path += '\\bin' print(sumo_path) os.makedirs('simple_2d', exist_ok=True) newNet.writeNet(sumo_path, 'simple_2d\\2d') vehTypes = [] vehTypes.append(VehicleType((0, 1, 0), 'smart')) vehTypes.append(VehicleType((1, 0, 0), 'dumb')) numCars = 100 starts = Demand._def_rand_departs(numCars, 2) prob_smart = .5 vehicles = [] dumbcars = [] for x in [('%s' % i, random.random(), starts[i]) for i in range(numCars)]: if x[1] < prob_smart: vType = vehTypes[0] else: vType = vehTypes[1] dumbcars.append(x[0]) route = Route(['0_1', '1_2']) vehicles.append(Vehicle(x[0], x[2], vType, route)) dem = Demand(vehTypes, vehicles) dem.genRouteFile('simple_2d\\2d.rou.xml') sensorList = [] lane = '1_2_0' sensorList.append(Fusion.Sensor('first', 75, lane, [.25], .95)) sensorList.append(Fusion.Sensor('second', 200, lane, [.25], .95)) fus = Fusion.FusionArchitecture(sensorList, None, None) runSim.runSim('simple_2d\\2d.net.xml', 'simple_2d\\2d.rou.xml', None, fusion=fus)
def mapa(request): idProduto = request.GET.get("idProduto", None) markedSectors = [] if idProduto is not None: urlSetoresProduto = 'https://scan-skip-plu-teste.herokuapp.com/setoresProduto/' + idProduto network = Network() response = network.request(urlSetoresProduto, 'GET') if response != -1: for setor in response: markedSectors.append(setor['idSetor']) return render(request, 'mapa.html', { 'markedSectors': markedSectors, 'sectorsRange': range(1, 53) })
def produto(request, idProduto): urlProduto = 'https://supermercado-carrinho.herokuapp.com/produtos/TesteProduto/' #'http://143.107.102.48:3000/produto/' + idProduto network = Network() response = network.request(urlProduto, 'GET') nome = response['nome'] marca = response['marca'] preco = response['valor'] preco = ('%.2f' % (float(preco) / 100)).replace('.', ',') categoria = response['categoria'] imagem = response['imagem'] return render( request, 'produto-escaneado-confirmacao.html', { 'idProduto': idProduto, 'nome': nome, 'marca': marca, 'preco': preco, 'categoria': categoria, 'imagem': imagem })
def create_population(self, count): """Create a population of random networks. Args: count (int): Number of networks to generate, aka the size of the population Returns: (list): Population of network objects """ pop = [] for _ in range(0, count): # Create a random network. network = Network(self.nn_param_choices) network.create_random() # Add the network to our population. pop.append(network) return pop
def produto(request, idProduto): urlProduto = 'https://scan-skip-plu-teste.herokuapp.com/produto/' + idProduto network = Network() response = network.request(urlProduto, 'GET') if response != -1: quantidade = 1 nome = response['nome'] marca = response['marca'] preco = response['valor'] preco = ('%.2f' % (float(preco)/100)).replace('.', ',') categoria = response['categoria'] imagem = response['imagem'] # Salvando ultimo produto escaneado na sessão request.session['lastScannedProduct'] = idProduto return render(request, 'produto-escaneado-confirmacao.html', {'idProduto': idProduto, 'nome': nome, 'marca': marca, 'preco': preco, 'categoria': categoria, 'imagem': imagem, 'quantidade': quantidade, 'registrado': True}) else: nome = 'Produto não registrado' marca = '-------' preco = '-------' categoria = '-------' return render(request, 'produto-escaneado-confirmacao.html', {'idProduto': idProduto, 'nome': nome, 'marca': marca, 'preco': preco, 'categoria': categoria, 'registrado': False})
def main(arguments): print("Loading client...") try: if len(arguments) != globalsettings.ARGUMENTS_LENGTH: raise Exception(f"required 3 arguments received {len(arguments)}.") client_frame = window_frame( client_app, "uSpeak - client", GetSystemMetrics(0) / 2 - globalsettings.CLIENT_SCREEN_WIDTH / 2, GetSystemMetrics(1) / 2 - globalsettings.CLIENT_SCREEN_HEIGHT / 2, globalsettings.CLIENT_SCREEN_WIDTH, globalsettings.CLIENT_SCREEN_HEIGHT, stylesheet='', resizable=False) chat_box = text_field(client_frame, 10, 32, '') chat_box.setReadOnly(True) chat_box.resize(470, 350) userlist = QListWidget(client_frame) userlist.move(520, 32) userlist.resize(100, 120) channellist = QListWidget(client_frame) channellist.itemClicked.connect( lambda item: event.set_selected_channel(item)) channellist.move(520, 160) channellist.resize(100, 200) chat_input = input_field(client_frame, 10, 387, 'Message #selected channel') chat_input.resize(470, 30) message_button = button( client_frame, "Send message", 520, 387, lambda: client.send_message( globalsettings.USER_MESSAGE, event.selected_channel, event.get_input_text())) chat_input.returnPressed.connect(message_button.click) exitAct = QAction(QIcon(''), '&Disconnect', client_frame) exitAct.triggered.connect(os._exit) menubar = client_frame.menuBar() server_menu = menubar.addMenu('Server') action = server_menu.addAction(exitAct) event = Event(chat_box, chat_input, userlist, channellist) client = Network(arguments[0], int(arguments[1]), arguments[2], event) client.establish_connection() time.sleep(0.3) event.set_selected_channel(channellist.item(0)) client_frame.gui_show() except Exception as exception: print(f"{exception}") print('test') exit() except IndexError: print(f"Required 3 arguments received {len(arguments)}.") exit() except: pass
Node('6', 0, 225), Node('7', 0, 250) ] edges = [ Edge(nodes[0], nodes[1]), Edge(nodes[1], nodes[2]), Edge(nodes[1], nodes[3]), Edge(nodes[2], nodes[4], speed=5), Edge(nodes[3], nodes[5], speed=2), Edge(nodes[4], nodes[6]), Edge(nodes[5], nodes[6]), Edge(nodes[6], nodes[7], numLanes=2) ] newNet = Network(nodes, edges) sumo_path = 'C:\\dev\\Traffic\\Sumo\\bin' os.makedirs('oneway_data', exist_ok=True) newNet.writeNet(sumo_path, 'oneway_data\\oneway') vehTypes = [] vehTypes.append(VehicleType((0, 1, 0), 'smart')) vehTypes.append(VehicleType((1, 0, 0), 'dumb')) numCars = 100 starts = Demand._def_rand_departs(numCars, 2) prob_smart = .5 vehicles = []
ap.add_argument("-APRILTAG_16h5", "--DICT_APRILTAG_16h5", required=False, action="store_true", default=False, help="Include DICT_APRILTAG_16h5 into dataset.") ap.add_argument("-APRILTAG_25h9", "--DICT_APRILTAG_25h9", required=False, action="store_true", default=False, help="Include DICT_APRILTAG_25h9 into dataset.") ap.add_argument("-APRILTAG_36h10", "--DICT_APRILTAG_36h10", required=False, action="store_true", default=False, help="Include DICT_APRILTAG_36h10 into dataset.") ap.add_argument("-APRILTAG_36h11", "--DICT_APRILTAG_36h11", required=False, action="store_true", default=False, help="Include DICT_APRILTAG_36h11 into dataset.") args = vars(ap.parse_args()) # Verify the passed parameters if not isinstance(args["shape"], tuple) or len(args["shape"]) != 3: raise Exception("Shape parameter is invalid. Should be something like '(256,256,3)'.") if not isinstance(args["learning_rate"], float) or args["learning_rate"] <= 0.0: raise Exception("Learning rate parameter is invalid. Should be a float bigger than '0.0'.") if not isinstance(args["iterations"], int) or args["iterations"] < 1: raise Exception("Iterations has an invalid value.") if not isinstance(args["steps"], int) or args["steps"] < 1: raise Exception("Steps argument has an invalid value.") if not isinstance(args["batch_size"], int) or args["batch_size"] < 1: raise Exception("Batch size has an invalid value.") if not os.path.isdir(os.path.dirname(args["weights"])): raise Exception("Path to store weights is invalid.") if not isinstance(args["saving_iterations"], int) or args["saving_iterations"] < 1: raise Exception("Saving iterations has an invalid value.") if not (args["DICT_4X4_50"] or args["DICT_4X4_100"] or args["DICT_4X4_250"] or args["DICT_4X4_1000"] or args["DICT_5X5_50"] or args["DICT_5X5_100"] or args["DICT_5X5_250"] or args["DICT_5X5_1000"] or args["DICT_6X6_50"] or args["DICT_6X6_100"] or args["DICT_6X6_250"] or args["DICT_6X6_1000"] or args["DICT_7X7_50"] or args["DICT_7X7_100"] or args["DICT_7X7_250"] or args["DICT_7X7_1000"] or args["DICT_ARUCO_ORIGINAL"] or args["DICT_APRILTAG_16h5"] or args["DICT_APRILTAG_25h9"] or args["DICT_APRILTAG_36h10"] or args["DICT_APRILTAG_36h11"]): raise Exception("At least one marker family need to be enabled.") # Initalize the segnet network network = Network(shape=args["shape"], learning_rate=args["learning_rate"]) # Load checkpoint if is setted if args["checkpoint"] != '': network.load_weights(weights_path=args["weights"]) # Start training network.train_synthetic_data(iterations=args["iterations"], steps=args["steps"], batch_size=args["batch_size"], weights_path=args["weights"], saving_iterations=args["saving_iterations"], DICT_4X4_50=args["DICT_4X4_50"], DICT_4X4_100=args["DICT_4X4_100"], DICT_4X4_250=args["DICT_4X4_250"], DICT_4X4_1000=args["DICT_4X4_1000"], DICT_5X5_50=args["DICT_5X5_50"], DICT_5X5_100=args["DICT_5X5_100"], DICT_5X5_250=args["DICT_5X5_250"], DICT_5X5_1000=args["DICT_5X5_1000"], DICT_6X6_50=args["DICT_6X6_50"], DICT_6X6_100=args["DICT_6X6_100"], DICT_6X6_250=args["DICT_6X6_250"], DICT_6X6_1000=args["DICT_6X6_1000"], DICT_7X7_50=args["DICT_7X7_50"], DICT_7X7_100=args["DICT_7X7_100"], DICT_7X7_250=args["DICT_7X7_250"], DICT_7X7_1000=args["DICT_7X7_1000"], DICT_ARUCO_ORIGINAL=args["DICT_ARUCO_ORIGINAL"], DICT_APRILTAG_16h5=args["DICT_APRILTAG_16h5"], DICT_APRILTAG_25h9=args["DICT_APRILTAG_25h9"], DICT_APRILTAG_36h10=args["DICT_APRILTAG_36h10"], DICT_APRILTAG_36h11=args["DICT_APRILTAG_36h11"])
help="Define the shape of a tile (default=(256,256,3))).") ap.add_argument("-w", "--weights", required=False, default='./weights/weights.ckpt', help="Path to the weights (default='./weights/weights.ckpt').") ap.add_argument("-o", "--output", required=True, help="Path to save prediction.") args = vars(ap.parse_args()) # Verify the passed parameters if not os.path.isfile(args["image"]): raise Exception("Path to image is invalid.") if not isinstance(args["shape"], tuple) or len(args["shape"]) != 3: raise Exception( "Shape parameter is invalid. Should be something like '(256,256,3)'.") if not os.path.isdir(os.path.dirname(args["weights"])): raise Exception("Path to weights is invalid.") # Load the image image = cv2.cvtColor(cv2.imread(args["image"], 3), cv2.COLOR_BGR2RGB) # Initalize the segnet network network = Network(shape=args["shape"]) # Load the weights network.load_weights(weights_path=args["weights"]) # Start prediction and save the results prediction = network.predict(image) cv2.imwrite(args["output"], cv2.cvtColor(prediction, cv2.COLOR_RGB2BGR))
dataset = [] index_count = 0 current_sample = args["dataset_path"] + "/" + str(index_count) + ".jpg" current_sample_gt = args["dataset_path"] + "/" + str(index_count) + "_gt.jpg" while os.path.isfile(current_sample) and os.path.isfile(current_sample_gt): dataset.append([current_sample, current_sample_gt]) index_count += 1 current_sample = args["dataset_path"] + "/" + str(index_count) + ".jpg" current_sample_gt = args["dataset_path"] + "/" + str( index_count) + "_gt.jpg" if len(dataset) <= 0: raise Exception( "Cannot finde training samples in the directory " + args["dataset_path"] + ".\r\n To get more information take a look at the original github repo." ) # Initalize the segnet network network = Network(shape=args["shape"], learning_rate=args["learning_rate"]) # Load checkpoint if is setted if args["checkpoint"] != '': network.load_weights(weights_path=args["weights"]) # Start training network.train_real_data(iterations=args["iterations"], steps=args["steps"], batch_size=args["batch_size"], weights_path=args["weights"], saving_iterations=args["saving_iterations"], dataset=dataset)
def main(): server_settings = config.load_config(globalsettings.CONFIG_FILE_LOCATION) server = Network(server_settings['hostname'], server_settings['port'], server_settings['max_users'], server_settings['rcon'], server_settings['channels']) server.start()
# Acquisition of Data: FOV, Cropped Original Image and Whole Image print('Data Generation') print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) Preprocessing = preprocessing.Data(options.train_image, options.train_label, options.train_probability_map, C) FOV_Path, LABEL_Path, IMAGE_Path, FOV_size, Fov_num = Preprocessing.create_data( options.path) print('Data Generation finished') print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) # Input of 2 models FOV_input = Input(shape=FOV_size) Stack_input = Input(shape=FOV_size) # Network of two CNNs Net = Network(options.network, options.structure) First_Network_Output = Net.get_first_network(FOV_input) Second_Network_Output = Net.get_second_network(Stack_input) # Build the model First_Model = Model(FOV_input, First_Network_Output) Second_Model = Model(Stack_input, Second_Network_Output) # print(First_Model.summary()) # load the weight if options.input_weight_path: First_Model.load_weights(options.input_weight_path[0], by_name=True) Second_Model.load_weights(options.input_weight_path[1], by_name=True) # Complie the model optimizer = RMSprop(lr=1e-5)