Wyświetlanie zdj pizzy po wejściu na górny prawy róg
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120
tiles.py
120
tiles.py
@ -18,7 +18,6 @@ from keras.layers import Dense, Dropout, Activation, Flatten
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from keras.layers import Conv2D, MaxPooling2D
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import math
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pygame.init()
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display = pygame.display.set_mode((640, 640))
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@ -31,6 +30,7 @@ waiterImgR = pygame.image.load("waiterR.png")
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tableImg = pygame.image.load('table.png')
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chairImg = pygame.image.load('chair.png')
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clientImg = pygame.image.load('client.png')
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image = pygame.image.load('test/0.jpg')
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class Waiter:
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def __init__(self, loc):
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@ -62,6 +62,7 @@ class Waiter:
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def right(self):
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self.loc[0] += 32
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class Client:
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def __init__(self, loc):
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self.loc = loc
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@ -69,6 +70,7 @@ class Client:
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def render(self, surface):
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surface.blit(clientImg, (self.loc[0], self.loc[1]))
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def generate_client():
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loc_for_client = []
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for i in chairs:
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@ -78,6 +80,7 @@ def generate_client():
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client_coordinates = (loc_for_client[loc])
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return client_coordinates
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class Table:
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def __init__(self, loc, num):
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self.loc = loc
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@ -93,7 +96,8 @@ class Table:
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def get_number_of_chairs(self):
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return self.num_of_chairs
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def check_collision_with_table(x,y):
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def check_collision_with_table(x, y):
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answer = False
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for i in tables_coordinates:
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if i[0] <= x <= i[0] + 32 and i[1] <= y <= i[1] + 32:
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@ -110,6 +114,7 @@ class Chair:
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def render(self, surface):
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surface.blit(chairImg, (self.loc[0], self.loc[1]))
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def pos_of_chair(table_cor, k):
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pos = ((table_cor[0], table_cor[1] - 32),
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(table_cor[0], table_cor[1] + 32),
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@ -117,7 +122,8 @@ def pos_of_chair(table_cor, k):
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(table_cor[0] + 32, table_cor[1]))
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return pos[k]
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def check_collision_with_chair(x,y):
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def check_collision_with_chair(x, y):
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answer = False
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for i in chairs:
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for j in i:
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@ -143,6 +149,7 @@ class Food:
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def get_food(self):
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return self.name, self.price
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class Menu:
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def __init__(self, card={}):
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self.card = card
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@ -153,6 +160,7 @@ class Menu:
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def add_to_card(self, dish):
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self.card[str(len(self.card) + 1)] = dish
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class Order(Table):
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def __init__(self, status=False, table=0, dishes=[]):
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self.table = table
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@ -166,6 +174,7 @@ class Order(Table):
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def deliver(self):
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self.status = True
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class Graph:
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def __init__(self, num_of_nodes, directed=True):
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self.m_num_of_nodes = num_of_nodes
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@ -209,6 +218,7 @@ class Graph:
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path.reverse()
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return path
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def pathFromTo(start, dest):
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tab2 = [42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57]
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tab4 = [41, 58]
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@ -250,6 +260,7 @@ def pathFromTo(start, dest):
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path = graph.bfs(start, dest)
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return path
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def numToX(num):
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digits = [int(a) for a in str(num)]
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if num >= 100:
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@ -265,14 +276,17 @@ def numToX(num):
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x = x * 32
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return x
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def numToY(num):
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y = (math.floor(num / 20)) * 32
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return y
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def coordsToNum(coords):
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num = ((coords[1] // 32) * 20) + (coords[0] // 32)
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return int(num)
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def waiterGo(dest):
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print("Path: ", (pathFromTo(coordsToNum(waiter.loc), dest)))
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go = []
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@ -305,6 +319,7 @@ def waiterGo(dest):
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pygame.display.update()
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time.sleep(0.2)
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def mouseToNum():
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x = pygame.mouse.get_pos()[0]
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y = pygame.mouse.get_pos()[1]
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@ -312,6 +327,7 @@ def mouseToNum():
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squareNum = coordsToNum(cordTab)
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return squareNum
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class Map:
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def __init__(self):
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self.arr = np.zeros((20, 20))
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@ -326,6 +342,7 @@ class Map:
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def get_arr(self):
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return self.arr.transpose()
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class Tile():
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def __init__(self, parent=None, loc=None):
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self.parent = parent
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@ -337,6 +354,7 @@ class Tile():
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def __eq__(self, other):
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return self.position == other.position
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def astar(map, start, end):
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start_tile = Tile(None, start)
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start_tile.g = start_tile.h = start_tile.f = 0
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@ -379,7 +397,8 @@ def astar(map, start, end):
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skip = True
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if skip is False:
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child.g = current_tile.g + map[child.position[0]][child.position[1]]
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child.h = np.absolute(child.position[0] - end_tile.position[0]) + np.absolute(child.position[1] - end_tile.position[1])
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child.h = np.absolute(child.position[0] - end_tile.position[0]) + np.absolute(
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child.position[1] - end_tile.position[1])
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child.f = child.g + child.h
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for open_node in open_list:
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if child == open_node and child.g > open_node.g:
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@ -388,19 +407,20 @@ def astar(map, start, end):
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def tell_preferences():
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possibilities = [[30, 40 ,50, None],
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[True, False, None],
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[True, False, None],
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["high", "low", None],
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["tomato", "olives", "feta", None],
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["sausage", "pineapple", "mushrooms", "shrimps", "salami", None],
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[True, False, None]]
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possibilities = [[30, 40, 50, None],
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[True, False, None],
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[True, False, None],
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["high", "low", None],
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["tomato", "olives", "feta", None],
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["sausage", "pineapple", "mushrooms", "shrimps", "salami", None],
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[True, False, None]]
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choices = []
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for i in possibilities:
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choices.append(random.choice(i))
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return choices
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def evaluate_preferences(preferences):
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data = []
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if preferences[0] == 30:
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@ -499,11 +519,13 @@ def choose_pizza(prefernce):
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return ans
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def append_df_to_excel(df, excel_path):
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df_excel = pd.read_excel(excel_path)
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result = pd.concat([df_excel, df], ignore_index=True)
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result.to_excel(excel_path, index=False)
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def append_choice(ans, pre, d, df):
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new_row = pre
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new_row.append(list(d.keys())[list(d.values()).index(int(ans))])
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@ -540,15 +562,21 @@ def append_choice(ans, pre, d, df):
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n_df = pd.DataFrame(data, index=[len(df) + 1])
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append_df_to_excel(n_df, "restaurant.xlsx")
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def get_pizza(number):
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with open("dishes.json") as f:
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data = json.load(f)
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for i in data:
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if i["pos_in_card"] == int(number):
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food = Food(i['name'], i['pos_in_card'], i['price'], i['spiciness'], i['vege'], i['size'], i['allergens'], i['ingridients'], i['drink_in'])
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food = Food(i['name'], i['pos_in_card'], i['price'], i['spiciness'], i['vege'], i['size'], i['allergens'],
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i['ingridients'], i['drink_in'])
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return food
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#network
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def display_img(surface, image):
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surface.blit(image, (120, 120))
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def create_training_data():
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DATADIR = "Images"
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CATEGORIES = ["yes", "no"]
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@ -575,10 +603,11 @@ def create_training_data():
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y = np.array(y)
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print("Training data created!")
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return X,y
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return X, y
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def learn_neural_network(X,y):
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X = X/255.0
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def learn_neural_network(X, y):
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X = X / 255.0
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model = Sequential()
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@ -605,16 +634,19 @@ def learn_neural_network(X,y):
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return model
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def prepare_img(filepath):
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IMG_SIZE = 90
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img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
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new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
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return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1) / 255
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def predict(model):
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return model.predict([prepare_img('directory')])
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def reusult(prediction):
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def result(prediction):
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if prediction[0][0] >= 0.5:
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print(math.ceil(prediction[0][0]))
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print('No pepperoni')
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@ -623,16 +655,30 @@ def reusult(prediction):
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print("Pepperoni")
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#####################neural network##############################
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DATADIR = "C:/Datasets/Ingridients"
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CATEGORIES = ["yes", "no"]
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IMG_SIZE = 90
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training_data = []
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create_training_data()
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X = []
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y = []
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for features, label in training_data:
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X.append(features)
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y.append(label)
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X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
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y = np.array(y)
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"""
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m = learn_neural_network(X, y)
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prediction = m.predict([prepare_img('p1.jpg')])
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print(prediction[0][0])
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result(prediction)
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"""
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#######################################################
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map = Map()
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waiter = Waiter([32, 32])
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@ -656,8 +702,11 @@ for table in tables:
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client = Client(generate_client())
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def main():
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direction = []
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first_time = True
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number = 0
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while True:
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clock.tick(10)
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@ -681,6 +730,7 @@ def main():
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for chair_list in chairs:
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for chair in chair_list:
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chair.render(display)
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key = pygame.key.get_pressed()
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left, middle, right = pygame.mouse.get_pressed()
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if middle:
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@ -689,7 +739,9 @@ def main():
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while True:
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x = pygame.mouse.get_pos()[1]
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y = pygame.mouse.get_pos()[0]
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if y > 608 or y < 32 or x > 608 or x < 32 or check_collision_with_table(y,x) or check_collision_with_chair(y, x):
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if y > 608 or y < 32 or x > 608 or x < 32 or check_collision_with_table(y,
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x) or check_collision_with_chair(
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y, x):
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print("I can't go there")
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break
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goal = (x // 32, y // 32)
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@ -715,16 +767,18 @@ def main():
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break
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print()
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print("Hello Sir, tell me yours preferences")
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print("Pass: 'budget', 'spiciness', 'vege', 'level_of_hunger', 'allergy', 'favorite_ingridient', 'drink_in'\n")
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print(
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"Pass: 'budget', 'spiciness', 'vege', 'level_of_hunger', 'allergy', 'favorite_ingridient', 'drink_in'\n")
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print("Here is my list of preferences")
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ingridients = tell_preferences()
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print(ingridients)
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print()
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pizza = get_pizza(choose_pizza(evaluate_preferences(ingridients)))
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print("Our proposition:")
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print("Name = {}\nprice = {}\nspiciness = {}\nvege = {}\nsize = {}\nallergens = {}\ningridients = {}\ndrink_in = {}\n"
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.format(pizza.name,pizza.price, pizza.spiciness,pizza.vege,pizza.size,pizza.allergens,pizza.ingridients,pizza.drink_in))
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print(
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"Name = {}\nprice = {}\nspiciness = {}\nvege = {}\nsize = {}\nallergens = {}\ningridients = {}\ndrink_in = {}\n"
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.format(pizza.name, pizza.price, pizza.spiciness, pizza.vege, pizza.size, pizza.allergens,
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pizza.ingridients, pizza.drink_in))
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if len(direction) > 0:
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d = direction.pop(0)
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@ -742,6 +796,16 @@ def main():
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pass
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waiter.render(display)
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client.render(display)
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if waiter.loc == [576, 32]:
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if first_time:
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number = np.random.randint(20)
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image = pygame.image.load('test/' + str(number) + '.jpg')
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first_time = False
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display_img(display, image)
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else:
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first_time = True
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pygame.display.update()
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