added image recognition
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karoel2
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__pycache__/choice_tree.cpython-36.pyc
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__pycache__/choice_tree.cpython-36.pyc
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__pycache__/data.cpython-36.pyc
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__pycache__/data.cpython-36.pyc
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food_10_64x3_test.hdf5
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food_10_64x3_test.hdf5
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food_10_64x3_test.txt
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food_10_64x3_test.txt
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main.py
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main.py
@ -10,8 +10,64 @@ import numpy as np
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from data import *
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from choice_tree import *
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import tensorflow as tf
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from keras import *
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import h5py
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pygame.init()
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WIN = 0
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LOSSE = 0
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DEFINE = 0
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IMG_SIZE = 64
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COLOR_CHANNELS = 3
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CATEGORIES = [
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"apple_pie",
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"club_sandwich",
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"greek_salad",
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"hamburger",
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"hot_dog",
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"ice_cream",
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"lasagna",
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"pizza",
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"steak",
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"waffles"
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]
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model = tf.keras.models.load_model('final1')
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with h5py.File('food_10_64x3_test.hdf5', "r") as f:
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a_group_key = list(f.keys())[0]
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data = list(f[a_group_key])
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# print(len(data))
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data = np.array(data)
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X = np.array(data).reshape(-1, 64, 64, 3)
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with open('food_10_64x3_test.txt', 'r') as f:
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y = f.read().split()
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temp = []
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for item in y:
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temp.append(int(item))
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# print(len(X))
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# print(len(y))
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menu = []
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for i in range(len(X)):
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menu.append([X[i], y[i]])
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random.shuffle(menu)
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def image_recognition():
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LOSSE += 1
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for _ in range(100):
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photo = random.choice(menu)
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prediction = model.predict(np.expand_dims(photo[0], axis=0))
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max_value = prediction[0].max()
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idx = np.where(prediction[0]==max_value)
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if CATEGORIES[idx[0][0]] == waiter.order_list[-1]:
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WIN += 1
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break
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print(WIN, LOSSE - WIN)
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# ai settings
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#S_IDLE = ("kitchen", "middle", "inplace")
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#S_FIRST = ("order", "food")
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@ -160,6 +216,7 @@ class Agent:
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self.orders = []
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self.food = False
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self.goal = (0,0)
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self.order_list = []
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def walk(self):
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if self.path:
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@ -494,6 +551,8 @@ while True:
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if not waiter.orders and restaurant.tiles[waiter.y][waiter.x].clientState == "order" and not waiter.path:
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restaurant.tiles[waiter.y][waiter.x].clientState = "wait"
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waiter.orders = (waiter.x, waiter.y)
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DEFINE += 1
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waiter.order_list.append(random.choice(CATEGORIES))
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if (waiter.x, waiter.y) == KITCHEN:
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if waiter.orders:
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restaurant.kitchen.append([waiter.orders[0], waiter.orders[1], 50])
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@ -501,6 +560,7 @@ while True:
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elif not waiter.food:
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for t in restaurant.kitchen:
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if not t[2]:
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image_recognition()
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waiter.BFS((t[0], t[1]))
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restaurant.kitchen.remove(t)
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waiter.food = True
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