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Author SHA1 Message Date
Tao-Sen Chang
a7152dff3c Individual Project #2; s442720 2020-05-10 12:44:35 +00:00
Tao-Sen Chang
d9a5f26c02 Individual Project #2; s442720 2020-05-10 12:42:43 +00:00
Tao-Sen Chang
f3ddde84e3 Individual Project #2; s442720 2020-05-10 12:27:58 +00:00
Tao-Sen Chang
2a1724a4b7 Individual Project #2; s442720 2020-05-10 12:27:43 +00:00
Tao-Sen Chang
71dc3e81a2 Individual Project #2; s442720 2020-05-10 12:27:15 +00:00
Tao-Sen Chang
a73862b48b Individual Project #2; s442720 2020-05-10 12:17:01 +00:00
Martyna Drumińska
7958ec4a7e Merge branch 'master' of s444523/Waiter_group into master 2020-05-04 09:46:45 +00:00
Weronika Skowrońska
cf203978c6 Waiter group 
Report from my individual part
 2020-05-04 06:34:26 +00:00
Martyna Drumińska
21b2f6328b Merge branch 'master' of s444523/Waiter_group into master 2020-04-30 07:52:50 +00:00
Weronika Skowrońska
74e6baecfa Individual Project #1 implementation; s444523 
A Convoultional Neural Network classyfing customers plates into three categories. Documentation inside s444523.rar
 2020-04-30 07:42:18 +00:00
Weronika Skowrońska
08def183f7 Individual Project #1 implementation 
CNN classifying images of plates
 2020-04-29 19:47:30 +00:00
5 changed files with 0 additions and 1679 deletions
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354
AI_projecty.py
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@ -1,354 +0,0 @@
import pandas as pd
import pygame
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from pygame.locals import *
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree
import random
from textpygame import get_key, ask, display_box
#read csv file
dataset= pd.read_csv("veganism.csv")
#show shape of dataset
print(dataset.shape)
#create a new dataset
newdataset = pd.DataFrame(dataset, columns=['ethnicity', 'gender', 'appearence', 'vegan'])
# creating instance of labelencoder
labelencoder = LabelEncoder()
# Assigning numerical values and storing in another column
newdataset['ethnicity_no'] = labelencoder.fit_transform(newdataset['ethnicity'])
newdataset['gender_no']= labelencoder.fit_transform(newdataset['gender'])
newdataset['appearence_no']= labelencoder.fit_transform(newdataset['appearence'])
print(newdataset)
# for x values drop unimportant columns, axis=1 specifies that we want the columns not rows
Y=newdataset['vegan']
X=newdataset.drop(newdataset.columns[0:4], axis=1)
print(X,Y)
#test 14% of the data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)
classifier = DecisionTreeClassifier()
classifier.fit(X_train, Y_train)
y_pred = classifier.predict(X_test)
#ignores minority groups which results in 0 for prediction. to solve this i have to use smthing else
print(pd.DataFrame(confusion_matrix(Y_test, y_pred),columns=['Predicted not vegan', 'Predicted vegan'], index=['Actually not vegan', 'Actually vegan']))
#accuracy score is high as expected
print(accuracy_score(Y_test, y_pred))
fn=['ethnicity','gender','appearence']
cn=['yes', 'no']
# Setting dpi = 300 to make image clearer than default
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)
tree.plot_tree(classifier,
feature_names = fn,
class_names=cn,
filled = True);
fig.savefig('imagenamenew.png')
#using the my_grid3 program
# Colors:
# Define some colors
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
GREEN = (0, 255, 0)
RED = (255, 0, 0)
BLUE = (0, 0, 240)
MAGENTA=(255, 0, 255)
# Width and Height of each square:
WIDTH = 20
HEIGHT = 20
# Margin:
MARGIN = 5
grid = [[0 for x in range(16)] for y in range(16)]
def change_value(i, j, width, n):
for r in range(i, i + width):
for c in range(j, j + width):
grid[r][c] = n
if grid[r][c]==5:
break
class Table:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j, 2, 1)
class Kitchen:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j, 3, 2)
class Agent:
def __init__(self, orig_coordinate_i, orig_coordinate_j):
self.orig_coordinate_i = orig_coordinate_i
self.orig_coordinate_j = orig_coordinate_j
self.state = np.array([1, 2])
change_value(orig_coordinate_j, orig_coordinate_j, 1, 3)
self.state_update(orig_coordinate_i, orig_coordinate_j)
def state_update(self, c1, c2):
self.state[0] = c1
self.state[1] = c2
print(self.state)
def leave(self):
change_value(self.state[0], self.state[1], 1, 0)
def move_up(self):
self.leave()
self.state_update(x - 1, y)
change_value(self.state[0], self.state[1], 1, 3)
def move_down(self):
self.leave()
change_value(self.state[0], self.state[1], 1, 0)
self.state_update(x + 1, y)
change_value(self.state[0], self.state[1], 1, 3)
def move_right(self):
self.leave()
self.state_update(x, y + 1)
change_value(self.state[0], self.state[1], 1, 3)
def move_left(self):
self.leave()
self.state_update(x, y - 1)
change_value(self.state[0], self.state[1], 1, 3)
class Customer:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j,1 , 4)
class CustomerPlace:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j,1 , 5)
## default positions of the agent:
x = 11
y = 11
agent = Agent(x, y)
table1 = Table(2, 2)
table2 = Table(2, 7)
table3 = Table(2, 12)
table4 = Table(7, 2)
table5 = Table(7, 7)
table6 = Table(7, 12)
table7 = Table(12, 2)
table8 = Table(12, 7)
pygame.init()
# create a font object.
# 1st parameter is the font file
# which is present in pygame.
# 2nd parameter is size of the font
font = pygame.font.Font('freesansbold.ttf', 14)
X = 400
Y = 400
# create a text suface object,
# on which text is drawn on it.
text = font.render('waiter: hello, let me help you with your order.', True, WHITE, BLACK)
userText=font.render('user: ', True, BLUE, BLACK)
# create a rectangular object for the
# text surface object
textRect = text.get_rect()
inputRect = userText.get_rect()
# set the center of the rectangular object.
textRect.center= (200, 340)
inputRect.center=(200,370)
# class Kitchen:
kitchen = Kitchen(13, 13)
x=[2,7,12]
y=[2,7]
random_customer_seat_x=random.choice(x)
random_customer_seat_y=random.choice(y)
print(random_customer_seat_x,random_customer_seat_y)
seat=Customer(random_customer_seat_x,random_customer_seat_y)
next_to=CustomerPlace(random_customer_seat_x,random_customer_seat_y-1)
WINDOW_SIZE = [405, 405]
screen = pygame.display.set_mode(WINDOW_SIZE)
pygame.display.set_caption("Waiter_Grid3")
done = False
print(random_customer_seat_x,random_customer_seat_y-1)
clock = pygame.time.Clock()
#updating the drawing
def updateDraw():
x = agent.state[0]
y = agent.state[1]
screen.fill(BLACK) # Background color
for row in range(16): # Drawing the grid
for column in range(16):
color = WHITE
if grid[row][column] == 1:
color = GREEN
if grid[row][column] == 2:
color = RED
if grid[row][column] == 3:
color = BLUE
if grid[row][column] == 4:
color = MAGENTA
surface = pygame.draw.rect(screen,
color,
[(MARGIN + WIDTH) * column + MARGIN,
(MARGIN + HEIGHT) * row + MARGIN,
WIDTH,
HEIGHT])
def customer():
if x == random_customer_seat_x and y == random_customer_seat_y - 1:
screen.blit(text, textRect)
ethnicity3= ask(screen, question="ethnicity ")
gender3= ask(screen,question="gender ")
appearence3=ask(screen, question="appearence ")
if ethnicity3 == "black":
ethnicity3 = 1
elif ethnicity3 == "asian":
ethnicity3 = 0
else:
ethnicity3 = 2
if gender3 == "male":
gender3 = 0
else:
gender3 = 1
if appearence3 == "hippie":
appearence3= 0
else:
appearence3 = 1
prediction = classifier.predict([[ethnicity3, gender3, appearence3]])
pygame.quit()
if prediction == [0]:
print("You're probably not vegan. Would you like a regular menu?")
else:
print("It seems like youre vegan. Would you like a vegan menu?")
exit()
running=True
# pick a font you have and set its size
text2 = 'this text is editable'
font2 = pygame.font.SysFont(None, 48)
img2 = font.render(text2, True, RED)
rect2 = img2.get_rect()
rect2.topleft = (20, 20)
background = BLACK
# put the label object on the screen at point x=100, y=100
cursor = Rect(rect2.topright, (3, rect2.height))
while running:
x = agent.state[0]
y = agent.state[1]
pygame.time.delay(100)
for event in pygame.event.get(): # Checking for the event
if event.type == pygame.QUIT: # If the program is closed:
done = True # To exit the loop
keys = pygame.key.get_pressed() # Moving the agent:
if keys[pygame.K_LEFT]: # Left:
# Checking if not a table, a kitchen, or a wall:
if y - 1 < 0 or grid[x][y - 1] == 1:
continue
else:
agent.move_left() # If okay, the move
if keys[pygame.K_RIGHT]: # The same procedure with right:
if y + 1 > 15 or grid[x][y + 1] == 1 or grid[x][y + 1] == 2:
continue
else:
agent.move_right()
if keys[pygame.K_UP]: # The same procedure with up:
if x - 1 < 0 or grid[x - 1][y] == 1 or grid[x - 1][y] == 2:
continue
else:
agent.move_up()
if keys[pygame.K_DOWN]: # The same procedure with down
if x + 1 > 15 or grid[x + 1][y] == 1 or grid[x + 1][y] == 2:
continue
else:
agent.move_down()
updateDraw()
customer()
clock.tick(60) # Limit to 60 frames per second
pygame.display.flip() # Updating the screen

29
final-evaluation.md
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@ -1,29 +0,0 @@
# Final Evaluation - Waiter Project
Authors: Tao Sen Chang, Martyna Druminska, Weronika Skowronska.
The project aim is to simulate waiter's behaviour in a restaurant
# Features
The waiter is able to move from table to table, choosing the most optimal way; It can decide whether the customer wants vegan menu or not, and evaluate the customer's plate as empty (waiting to order) full (eating) or dirty (and waiting for receipt)
# Route choosing
Author: Tao Sen Chang
At beginning of our project, we use reinforcement learning to get the shortest route for our agent to traversal specific tables. Reinforcement learning is how software agent ought to take actions in an environment in order to maximize the notion of cumulative reward. The agent makes a sequence of decisions, and learns to perform the best actions every step. After tons of training loops, the agent could get the optimal strategy which might collect maximal reward. In the case of our program, the “maximal reward”, would be to find the minimum distance to the customer, which in turn would save time and effort.
# Plate evaluation
Author: Weronika Skowronska
For this part, we used CNNs to classify the plate on the table as empty, full or dirty. CNN is a kind of Neural Networks, used to work with pictures - it's good in extracting features from a photo, and therefore it demands less computations than a fully connected layer used for the same task.
As it is often hard to say, if the client has done eating - if the waiter is not sure, he asks.
The state of the table is then passed to the table object, and is available for further actions.
# Client evaluation
Author: Martyna Druminska
If the plate is classified as empty - which means that the client hasn't ordered yet - we want to propose him a menu. We used decision trees to make a prediction on whether the person would like a vegan menu or not, based on the “customers” appearance (answers in the console). The decision tree algorithm learns off of a dataset with a diverse set of variables to make the best prediction. The program generates a flow-like structure that represents the decisions, where each node is a “test” on an attribute. The decision tree finishes when there are only leaf nodes left.
# Conclusions
We tried to perform an action as similar as possible to a real-life situation. In our opinion, we did pretty well - we managed to create a program that runs efficiently and cohesively to satisfy the needs of the customer.
One remark is that our program has little to do about clients' orders and the paying process - we think that one more subproject could solve this problem.

67
textpygame.py
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@ -1,67 +0,0 @@
import pygame
import pygame.font
import pygame.event
import pygame.draw
import os
import sys
from pygame.locals import *
bad_words_file = os.path.os.path.dirname(os.path.realpath(sys.argv[0])) \
+ '/bad_words.txt'
def get_key():
while 1:
event = pygame.event.poll()
if event.type == KEYDOWN:
return event.key
else:
pass
def display_box(screen, message):
"Print a message in a box in the middle of the screen"
fontobject = pygame.font.Font(None, 18)
pygame.draw.rect(screen, (0, 0, 0),
((screen.get_width() / 2) - 100,
(screen.get_height() / 2) - 10,
200, 20), 0)
pygame.draw.rect(screen, (255, 255, 255),
((screen.get_width() / 2) - 102,
(screen.get_height() / 2) - 12,
204, 24), 1)
if len(message) != 0:
screen.blit(fontobject.render(message, 1, (255, 255, 255)),
((screen.get_width() / 2) - 100, (screen.get_height() / 2) - 10))
pygame.display.flip()
def ask(screen, question):
"ask(screen, question) -> answer"
pygame.font.init()
current_string = []
display_box(screen, question + ": " + "".join(current_string))
while 1:
inkey = get_key()
if inkey == K_BACKSPACE:
current_string = current_string[0:-1]
elif inkey == K_RETURN:
file = open(bad_words_file, 'r').readlines()
if "".join(current_string) in [thing[:-1] for thing in file]:
current_string = []
else:
break
elif inkey == K_MINUS:
current_string.append("_")
elif inkey <= 127:
current_string.append(chr(inkey))
display_box(screen, question + ": " + "".join(current_string))
return "".join(current_string)
def main():
screen = pygame.display.set_mode((320, 240))
print(ask(screen, "Name") + " was entered")
if __name__ == '__main__': main()

751
veganism.csv
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@ -1,751 +0,0 @@
ethnicity,gender,appearence,vegan
asian,male,hippie,0
asian,male,hippie,1
asian,male,hippie,1
asian,male,hippie,0
asian,male,hippie,1
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,1
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,male,regular,0
asian,female,hippie,1
asian,female,hippie,1
asian,female,hippie,1
asian,female,hippie,1
asian,female,hippie,1
asian,female,hippie,1
asian,female,hippie,1
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
asian,female,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,1
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,male,regular,0
black,female,hippie,1
black,female,hippie,1
black,female,hippie,1
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,1
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
black,female,regular,0
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,0
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,0
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,hippie,1
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
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white,male,regular,0
white,male,regular,0
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white,male,regular,0
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white,male,regular,0
white,male,regular,0
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white,male,regular,0
white,male,regular,0
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white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
white,male,regular,0
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478
waiter1406.py
View File

@ -1,478 +0,0 @@
#### MD #######
import pandas as pd
import pygame
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from pygame.locals import *
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree
import random
from textpygame import get_key, ask, display_box
###### /MD ######
import math
import pickle
########################
### WS ###
########################
# For CNN:
import keras
from keras.preprocessing import image
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
#initializing:
cnn_model = Sequential()
#Convolution:
cnn_model.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
#Pooling:
cnn_model.add(MaxPooling2D(pool_size = (2,2)))
# Adding a second convolutional layer
cnn_model.add(Convolution2D(32, 3, 3, activation = 'relu'))
cnn_model.add(MaxPooling2D(pool_size = (2, 2)))
#Flattening:
cnn_model.add(Flatten())
#Fully connected layers::
cnn_model.add(Dense(units = 128, activation = "relu"))
cnn_model.add(Dense(units = 3, activation = "softmax"))
# loading weigjts:
cnn_model.load_weights('s444523/best_model_weights2.h5')
#Making CNN:
cnn_model.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
########################
### /WS ###
########################
#### MD#####
#read csv file
dataset= pd.read_csv("veganism.csv")
#create a new dataset
newdataset = pd.DataFrame(dataset, columns=['ethnicity', 'gender', 'appearence', 'vegan'])
# creating instance of labelencoder
labelencoder = LabelEncoder()
# Assigning numerical values and storing in another column
newdataset['ethnicity_no'] = labelencoder.fit_transform(newdataset['ethnicity'])
newdataset['gender_no']= labelencoder.fit_transform(newdataset['gender'])
newdataset['appearence_no']= labelencoder.fit_transform(newdataset['appearence'])
# for x values drop unimportant columns, axis=1 specifies that we want the columns not rows
Y=newdataset['vegan']
X=newdataset.drop(newdataset.columns[0:4], axis=1)
#test 14% of the data
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.15)
classifier = DecisionTreeClassifier()
classifier.fit(X_train, Y_train)
y_pred = classifier.predict(X_test)
fn=['ethnicity','gender','appearence']
cn=['yes', 'no']
# Setting dpi = 300 to make image clearer than default (for the decision tree visualisation)
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)
tree.plot_tree(classifier,
feature_names = fn,
class_names=cn,
filled = True);
fig.savefig('imagenamenew.png')
class Customer:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j,1 , 4)
class CustomerPlace:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j,1 , 5)
###### /MD #######
# Colors:
# Define some colors
BLACK = (0, 0, 0)
WHITE = (255, 255, 255)
GREEN = (0, 255, 0)
RED = (255, 0, 0)
BLUE = (0, 0, 240)
YELLOW = (255, 255, 0)
#Width and Height of each square:
WIDTH = 20
HEIGHT = 20
#Margin:
MARGIN = 5
grid = [[0 for x in range(16)] for y in range(16)]
def change_value(i, j, width, n):
for r in range (i, i+width):
for c in range (j, j+width):
grid[r][c] = n
class Table:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j, 2, 1)
def get_destination_coor(self):
return [self.coordinate_i+2, self.coordinate_j+2]
########################
### WS ###
########################
# The finction "state of meal" chooses a photo of a plate at the given table.
def state_of_meal(self):
## !!!!!!###
num = np.random.randint(67, 100)
## !!!!!!###
if num<=67:
img_name = 'plates/{}.png'.format(num)
else:
img_name = 'plates/{}.jpg'.format(num)
return img_name
# The function "change state" changes the value of the state variable.
# It informs, whether the client are waiting for the waiter to make an order
# (0 - empty plates) are eating (2 - full plates) or are waiting for the
# waiter to get a recipt (1 - dirty plates)
def change_state(self, st):
self.state = st
########################
### /WS ###
########################
class Kitchen:
def __init__(self, coordinate_i, coordinate_j):
self.coordinate_i = coordinate_i
self.coordinate_j = coordinate_j
change_value(coordinate_i, coordinate_j, 3, 2)
class Agent:
def __init__(self,orig_coordinate_i, orig_coordinate_j):
self.orig_coordinate_i = orig_coordinate_i
self.orig_coordinate_j = orig_coordinate_j
self.state = np.array([orig_coordinate_i,orig_coordinate_j])
change_value(orig_coordinate_j, orig_coordinate_j, 1, 3)
self.state_update(orig_coordinate_i, orig_coordinate_j)
def state_update(self, c1, c2):
self.state[0] = c1
self.state[1] = c2
def leave(self):
change_value(self.state[0], self.state[1], 1, 0)
def move_to(self, nextPos):
self.leave()
nextPos_x, nextPos_y = nextPos
self.state_update(nextPos_x, nextPos_y)
change_value(self.state[0], self.state[1], 1, 3)
########################
### WS ###
########################
#The function "define_table" serches for the apropriate table in the
# table_dict (to enable the usage of class attributes and methods)
def define_table(self, t_num):
t_num = 'table{}'.format(t_num)
t_num = table_dict[t_num]
return t_num
# The function "check_plates" uses the pretrained CNN model to classify
# the plate on the table as empty(0), full(2) or dirty(1)
def check_plates(self, table_number):
table = self.define_table(table_number)
plate = table.state_of_meal()
plate= image.load_img(plate, target_size = (256, 256))
plate = image.img_to_array(plate)
#plate = plate.reshape((256, 256))
plate = np.expand_dims(plate, axis = 0)
result = cnn_model.predict(plate)[0]
print(result)
if result[1] == 1:
result[1] = 0
x = int(input("Excuse me, have You done eating? 1=Yes, 2 = No \n"))
result[x] = 1
for i, x in enumerate(result):
if result[i] == 1:
table.change_state(i)
return i
########################
### /WS ###
########################
def check_done():
for event in pygame.event.get(): # Checking for the event
if event.type == pygame.QUIT: # If the program is closed:
return True # To exit the loop
def draw_grid():
for row in range(16): # Drawing the grid
for column in range(16):
color = WHITE
if grid[row][column] == 1:
color = GREEN
if grid[row][column] == 2:
color = RED
if grid[row][column] == 3:
color = BLUE
pygame.draw.rect(screen,
color,
[(MARGIN + WIDTH) * column + MARGIN,
(MARGIN + HEIGHT) * row + MARGIN,
WIDTH,
HEIGHT])
## default positions of the agent:
x = 12
y = 12
agent = Agent(x, y)
table1 = Table(2, 2)
table2 = Table (2,7)
table3 = Table(2, 12)
table4 = Table(7, 2)
table5 = Table(7, 7)
table6 = Table(7, 12)
table7 = Table(12, 2)
table8 = Table(12, 7)
################### WS #####################
# I added the dict to loop through tables.
table_dict = {"table1":table1, "table2":table2, "table3":table3,"table4":table4,
"table5":table5,"table6":table6,"table7":table7,"table8":table8
}
################### WS #####################
pygame.init()
####MD ####
'''
# create a font object.
# 1st parameter is the font file
# which is present in pygame.
# 2nd parameter is size of the font
font = pygame.font.Font('freesansbold.ttf', 14)
X = 400
Y = 400
# create a text suface object,
# on which text is drawn on it.
text = font.render('waiter: hello, let me help you with your order.', True, WHITE, BLACK)
userText=font.render('user: ', True, BLUE, BLACK)
# create a rectangular object for the
# text surface object
textRect = text.get_rect()
inputRect = userText.get_rect()
# set the center of the rectangular object.
textRect.center= (200, 340)
inputRect.center=(200,370)
'''
#### /MD ####
#class Kitchen:
kitchen = Kitchen(13, 13)
WINDOW_SIZE = [405, 405]
screen = pygame.display.set_mode(WINDOW_SIZE)
pygame.display.set_caption("Waiter_Grid3")
done = False
clock = pygame.time.Clock()
with open('res_targets_4-1.data', 'rb') as filehandle:
# read the data as binary data stream
trained_route = pickle.load(filehandle)
destination = (4, 14)
trained_route.append(destination)
table_targets = [(9, 4),(4, 4),(4, 9),(4, 14)]
destination_tables = []
###### MD /########3
x=[2,7,12]
y=[2,7]
random_customer_seat_x=random.choice(x)
random_customer_seat_y=random.choice(y)
print(random_customer_seat_x,random_customer_seat_y)
seat=Customer(random_customer_seat_x,random_customer_seat_y)
next_to=CustomerPlace(random_customer_seat_x,random_customer_seat_y-1)
WINDOW_SIZE = [405, 405]
screen = pygame.display.set_mode(WINDOW_SIZE)
pygame.display.set_caption("Waiter_Grid3")
done = False
print(random_customer_seat_x,random_customer_seat_y-1)
clock = pygame.time.Clock()
#updating the drawing
def updateDraw():
x = agent.state[0]
y = agent.state[1]
screen.fill(BLACK) # Background color
for row in range(16): # Drawing the grid
for column in range(16):
color = WHITE
if grid[row][column] == 1:
color = GREEN
if grid[row][column] == 2:
color = RED
if grid[row][column] == 3:
color = BLUE
if grid[row][column] == 4:
color = MAGENTA
surface = pygame.draw.rect(screen,
color,
[(MARGIN + WIDTH) * column + MARGIN,
(MARGIN + HEIGHT) * row + MARGIN,
WIDTH,
HEIGHT])
def customer():
screen.blit(text, textRect)
ethnicity3 = input("Excuse me, What's you're ethnicity? <black>, <asian>, <white>\n")
gender3= input("Excuse me, What's you're gender? <male>, <female>, <other> \n")
appearence3=input("Excuse me, What's you're appearance? <hippie>, <other> \n")
if (ethnicity3 =="black"):
ethnicity3 = 1
elif ethnicity3 == "asian":
ethnicity3 = 0
else:
ethnicity3 = 2
if gender3 == "male":
gender3 = 0
else:
gender3 = 1
if appearence3 == "hippie":
appearence3= 0
else:
appearence3 = 1
prediction = classifier.predict([[ethnicity3, gender3, appearence3]])
#pygame.quit()
if prediction == [0]:
print("You're probably not vegan. Would you like a regular menu?")
else:
print("It seems like you're vegan. Would you like a vegan menu?")
#exit()
###### /MD ###
# -------- Main Program Loop -----------
exit_counter = 0
while not done:
screen.fill(BLACK) # Background color
draw_grid()
done = check_done()
new_route = trained_route[:]
for value in table_dict.values():
destination_tables.append(value.get_destination_coor())
num_of_table = 1
while len(new_route) != 0:
# move to next grid
agent.move_to(new_route[0])
# update the grid
pygame.time.delay(150)
screen.fill(BLACK)
draw_grid()
# Drawing the grid
clock.tick(100) # Limit to 60 frames per second
pygame.display.flip() # Updating the screen
# get current grid coordinate
x = agent.state[0]
y = agent.state[1]
# if reached the table, ask to collect the plates
if [x, y] in destination_tables:
########################
### WS ###
########################
#pygame.time.delay(100)
print("I'm at a table no. {}".format(num_of_table))
## Checking at what state are the plates:
state_of_table = agent.check_plates(num_of_table)
num_of_table +=1
if state_of_table == 0:
customer()
# Early stopping (after 10 rounds)
exit_counter += 1
print("exit_counter", exit_counter)
if exit_counter == 10:
play_again = 1
play_again = int(input("Exit? 0=No, 1=Yes \n"))
if play_again:
pygame.quit()
else:
exit_counter = 0
########################
### /WS ###
########################
destination_tables = destination_tables[1:]
new_route = new_route[1:]
# After each fool loop, we can quit the program:.
pygame.quit()