Individual Project #1 implementation; s444523

A Convoultional Neural Network classyfing customers plates into three categories. Documentation inside s444523.rar

waiter_v3.py

@@ -8,10 +8,37 @@ import math
 # For CNN:
 
 import keras
-from keras.models import load_model
 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"])
 
-saved_model = load_model('s444523/best_model.h5')
 
      ########################
      ###        WS        ###
@@ -53,7 +80,10 @@ class Table:
      
     # The finction "state of meal" chooses a photo of a plate at the given table.
     def state_of_meal(self):
-        num = np.random.randint(1, 102)
+        ## !!!!!!###
+        num = np.random.randint(67, 100)
+                ## !!!!!!###
+
         if num<=67:
             img_name = 'plates/{}.png'.format(num)
         else:
@@ -121,14 +151,18 @@ class Agent:
     def check_plates(self, table_number):
         table = self.define_table(table_number)
         plate = table.state_of_meal()        
-        clean_plate = image.load_img(plate, target_size = (256, 256))
-        clean_plate = image.img_to_array(clean_plate)
-        clean_plate = np.expand_dims(clean_plate, axis = 0)
-        result = saved_model.predict(clean_plate)[0]
+        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 x:
-                pred_class = i
-        table.change_state(pred_class)
+            if result[i] == 1:
+                table.change_state(i)
 
        ########################
        ###       /WS        ###
@@ -327,7 +361,7 @@ while not done:
         # 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"))
+            play_again = int(input("Exit? 0=No, 1=Yes \n"))
             if play_again:
                 pygame.quit()