Waiter group

Report from my individual part

CNN Plates Classification.md

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+# CNN Plates Classification
+Author: Weronika Skowrońska, s444523
+
+As my individual project, I decided to perform a classification of plates images using a Convolutional Neural Network. The goal of the project is to classify a photo of the client's plate as empty(0), dirty(1) or full(2), and assign an appropriate value to the given instance of the "Table" class.
+
+# Architecture
+
+Architecture of my CNN is very simple. I decided to use two convolutions, each using 32 feature detectors of size 3 by 3, followed by the ReLU activation function and MaxPooling of size 2 by 2.
+```sh
+classifier = Sequential()
+
+classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
+classifier.add(MaxPooling2D(pool_size = (2,2)))
+
+classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+classifier.add(Flatten())
+```
+After flattening, I added a fully connected layer of size 128 (again with ReLU activation function). The output layer consists of 3 neurons with softmax activation function, as I am using the Network for multiclass classification (3 possible outcomes).
+```sh
+classifier.add(Dense(units = 128, activation = "relu"))
+classifier.add(Dense(units = 3, activation = "softmax"))
+```
+The optimizer of my network is adam, and categorical cross entropy was my choice for a loss function.
+```sh
+classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
+```
+# Library
+
+I used keras to implement the network. It let me add some specific features to my network, such as early stopping and a few methods of data augmentation.
+```sh
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True,
+        width_shift_range=0.2,
+        height_shift_range=0.1,
+        fill_mode='nearest')
+```
+This last issue was very important to me, as I did not have many photos to train the network with (altogether there were approximately 1200 of them).
+
+# Project implementation
+
+After training the Network, I firstly saved the model which gave me the best results (two keras callbacks, EarlyStopping and ModelCheckpoint were very useful) to a file named "best_model.h5".
+```sh
+# callbacks:
+es = EarlyStopping(monitor='val_loss', mode='min', baseline=1, patience = 10)
+mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose = 1, period = 10)
+```
+It occured though, that the file is to big to upload it to git, so I modified the code a little bit, and instead of saving the model, I saved the weights:
+```sh
+mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose = 1, period = 10, save_weights_only = True)
+```
+To be honest, it was not a very good idea either, as the new file is also to big to upload it. I managed to solve the probem in another way: I added the h5 file to my google drive, and added a link to download it to the project files.
+
+To use the saved weights, I created the CNN model inside our project:
+```sh
+#initializing:
+classifier = Sequential()
+#Convolution:
+classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
+#Pooling:
+classifier.add(MaxPooling2D(pool_size = (2,2)))
+
+# Adding a second convolutional layer
+classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+#Flattening:
+classifier.add(Flatten())
+
+#Fully connected layers::
+classifier.add(Dense(units = 128, activation = "relu"))
+classifier.add(Dense(units = 3, activation = "softmax"))
+
+# loading weigjts:
+classifier.load_weights('s444523/best_model_weights2.h5')
+#Making CNN:
+classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
+```
+After coming to each table, the Agent (the waiter) evaluates a randomly selected photo of a plate using the provided model, and assigns the number of predicted class into the "state" attribute of a given table. This information will let perform further actions, based on the predicted outcome.
+
+I noticed that my program has difficulties in distinguishing a full plate from a dirty one - interestingly, this was also a problem for me and my friends when we worked as real waiters in the restaurant. Therefore, if the plate is classified by the waiter as dirty, he asks politely if the client already has done eating, and acts accordingly to his answer:
+```sh
+if result[1] == 1:
+            result[1] = 0
+            x = int(input("Excuse me, have You done eating? 1=Yes, 2 = No \n"))
+            result[x] = 1
+```

waiter_v3.py

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+import pygame
+import numpy as np
+import math
+
+     ########################
+     ###        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:
+classifier = Sequential()
+#Convolution:
+classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
+#Pooling:
+classifier.add(MaxPooling2D(pool_size = (2,2)))
+
+# Adding a second convolutional layer
+classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+#Flattening:
+classifier.add(Flatten())
+
+#Fully connected layers::
+classifier.add(Dense(units = 128, activation = "relu"))
+classifier.add(Dense(units = 3, activation = "softmax"))
+
+# loading weigjts:
+classifier.load_weights('s444523/best_model_weights2.h5')
+#Making CNN:
+classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
+
+
+     ########################
+     ###        WS        ###
+     ########################
+# Colors:
+# Define some colors
+BLACK = (0, 0, 0)
+WHITE = (255, 255, 255)
+GREEN = (0, 255, 0)
+RED = (255, 0, 0)
+BLUE = (0, 0, 240)
+
+#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
+    
+# the class "Table"        
+class Table:
+    def __init__(self, coordinate_i, coordinate_j, state = 0):
+        self.coordinate_i = coordinate_i
+        self.coordinate_j = coordinate_j
+        self.state = state
+        change_value(coordinate_i, coordinate_j, 2, 1)
+    def get_destination_coor(self):
+        return [self.coordinate_i, self.coordinate_j-1]
+    
+     ########################
+     ###        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([1,2])
+        change_value(orig_coordinate_j, orig_coordinate_j, 1, 3)
+        self.state_update(orig_coordinate_i, orig_coordinate_j)
+        self.previous_grid = np.array([-1, -1])
+        
+    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 previous_grid_update(self):
+        self.previous_grid[0] = self.state[0]
+        self.previous_grid[1] = self.state[1]
+    
+    def move_to(self, nextPos):
+        self.previous_grid_update()
+        self.leave()
+        self.state_update(x + nextPos[0], y + nextPos[1])
+        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 = np.expand_dims(plate, axis = 0)
+        result = classifier.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)
+
+       ########################
+       ###       /WS        ###
+       ########################
+    # check the next grid is not the previous grid to prevent the loop
+    def next_is_previous(self, x, y):
+        return np.array_equal(self.previous_grid, np.array([x, y]))
+
+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]) 
+
+# calculate the distance between two points 
+def distance(point1, point2):
+    return math.sqrt((point2[0] - point1[0])**2 + (point2[1] - point1[1])**2)
+
+## 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 #####################
+
+#class Kitchen:
+kitchen = Kitchen(13, 13)
+
+# destination array
+destination_tables = []
+
+
+pygame.init()
+WINDOW_SIZE = [405, 405]
+screen = pygame.display.set_mode(WINDOW_SIZE)
+
+pygame.display.set_caption("Waiter_Grid3")
+
+done = False
+
+clock = pygame.time.Clock()
+ 
+# -------- Main Program Loop -----------
+while not done:
+    screen.fill(BLACK)                      # Background color
+    
+    draw_grid()
+    done = check_done()
+    for value in table_dict.values(): destination_tables.append(value.get_destination_coor())
+    # We need to define the number of the table by which we are in:
+    
+    num_of_table = 1
+    while len(destination_tables) != 0:
+        
+        # set the first element(table) in array as currDestination
+        currDestination = destination_tables[0]
+        # from kitchen to table
+        while agent.state[0] != currDestination[0] or agent.state[1] != currDestination[1]:
+
+            #///////////////////////////////////////
+            x = agent.state[0]
+            y = agent.state[1]
+            
+            # set a huge default number
+            minDis = 9999
+            nextPos = []
+            # check whether the agent goes left
+            if y-1 >= 0 and grid[x][y-1] != 1 and not agent.next_is_previous(x, y-1):
+                minDis = distance([x, y-1], currDestination)
+                nextPos = [0, -1]  # means go left
+            
+            # check whether the agent goes right
+            if y+1 <= 15 and grid[x][y+1] != 1 and grid[x][y+1] != 2 and not agent.next_is_previous(x, y+1):  
+                d = distance([x, y+1], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [0, 1]  # means go right
+            
+            # check whether the agent goes up
+            if x-1 >= 0 and grid[x-1][y] != 1 and not agent.next_is_previous(x-1, y):
+                d = distance([x-1, y], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [-1, 0]  # means go up
+                    
+            # check whether the agent goes down
+            if x+1 <= 15 and grid[x+1][y] != 1 and grid[x+1][y] != 2 and not agent.next_is_previous(x+1, y):
+                d = distance([x+1, y], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [1, 0]  # means go down
+            
+#            print(agent.previous_grid)
+            agent.move_to(nextPos)
+            #////////////////////////////////////////////////
+            
+            pygame.time.delay(100)
+            screen.fill(BLACK)                      # Background color
+            draw_grid()                             # Drawing the grid
+            clock.tick(60)                           # Limit to 60 frames per second
+            pygame.display.flip()                    # Updating the screen
+        
+        
+        ########################
+        ###        WS        ###
+        ########################
+        #pygame.time.delay(100)
+        print("I'm at a table no. {}".format(num_of_table))
+        ## Checking at what state are the plates:
+        agent.check_plates(num_of_table)
+        num_of_table +=1
+        
+        ########################
+        ###       /WS        ###
+        ########################
+        # set the kitchen as currDestination 
+        currDestination = [13, 12]
+        # from table to kitchen
+        while agent.state[0] != currDestination[0] or agent.state[1] != currDestination[1]:
+
+            #///////////////////////////////////////
+            x = agent.state[0]
+            y = agent.state[1]
+            
+            # set a huge default number
+            minDis = 9999
+            nextPos = []
+            # check whether the agent goes left
+            if y-1 >= 0 and grid[x][y-1] != 1 and not agent.next_is_previous(x, y-1):
+                minDis = distance([x, y-1], currDestination)
+                nextPos = [0, -1]  # means go left
+            
+            # check whether the agent goes right
+            if y+1 <= 15 and grid[x][y+1] != 1 and grid[x][y+1] != 2 and not agent.next_is_previous(x, y+1):  
+                d = distance([x, y+1], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [0, 1]  # means go right
+            
+            # check whether the agent goes up
+            if x-1 >= 0 and grid[x-1][y] != 1 and grid[x-1][y] != 2 and not agent.next_is_previous(x-1, y):
+                d = distance([x-1, y], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [-1, 0]  # means go up
+                    
+            # check whether the agent goes down
+            if x+1 <= 15 and grid[x+1][y] != 1 and grid[x+1][y] != 2 and not agent.next_is_previous(x+1, y):
+                d = distance([x+1, y], currDestination)
+                if d < minDis:
+                    minDis = d
+                    nextPos = [1, 0]  # means go down
+            
+            agent.move_to(nextPos)
+            #////////////////////////////////////////////////
+            
+            pygame.time.delay(100)
+            screen.fill(BLACK)                      # Background color
+            draw_grid()                             # Drawing the grid
+            clock.tick(60)                           # Limit to 60 frames per second
+            pygame.display.flip()                    # Updating the screen
+            
+        
+        destination_tables = destination_tables[1:]    # remove the first element in the list
+        # After each fool loop, we can quit the program:.
+        if len(destination_tables) == 0:
+            play_again = 1
+            play_again = int(input("Exit? 0=No, 1=Yes \n"))
+            if play_again:
+                pygame.quit()
+   
+    
+pygame.quit()

which_plate_CNN.py

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+##My cnn, classyfing the plates as dirty, clean or full.
+#imports
+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
+from keras.callbacks import EarlyStopping
+from keras.callbacks import ModelCheckpoint
+
+#initializing:
+classifier = Sequential()
+
+#Convolution:
+classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
+
+#Pooling:
+classifier.add(MaxPooling2D(pool_size = (2,2)))
+
+# Adding a second convolutional layer
+classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+
+#Flattening:
+classifier.add(Flatten())
+
+#Fully connected layers::
+classifier.add(Dense(units = 128, activation = "relu"))
+classifier.add(Dense(units = 3, activation = "softmax"))
+
+#Making CNN:
+classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
+
+#From KERAS:
+from keras.preprocessing.image import ImageDataGenerator
+
+#Data augmentation:
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True,
+        width_shift_range=0.2,
+        height_shift_range=0.1,
+        fill_mode='nearest')
+
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+training_set = train_datagen.flow_from_directory('plates/training_set',
+										         target_size=(256, 256),
+										         batch_size=16,
+										         class_mode='categorical')
+
+test_set = test_datagen.flow_from_directory('plates/test_set',
+											 target_size=(256, 256),
+											 batch_size=16,
+											 class_mode='categorical')
+
+# callbacks:
+es = EarlyStopping(monitor='val_loss', mode='min', baseline=1, patience = 10)
+mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose = 1, period = 10)
+classifier.fit_generator(
+			        training_set,
+                    steps_per_epoch = 88,
+			        epochs=200,
+                    callbacks=[es, mc],
+			        validation_data=test_set,
+			        validation_steps=10)