neural network integration

Network/Network.py

@@ -1,23 +1,22 @@
 import tensorflow as tf
 from keras import layers
-from keras.models import Sequential
-from keras.optimizers import Adam
-from keras.utils import to_categorical
-from keras.preprocessing.image import ImageDataGenerator
 
- # Normalizes the pixel values of an image to the range [0, 1].
+# Normalizes the pixel values of an image to the range [0, 1].
+
 
 def normalize(image, label):
     return image / 255, label
+
+
  # Set the paths to the folder containing the training data
 train_data_dir = "Network/Training/"
- # Set the number of classes and batch size
+# Set the number of classes and batch size
 num_classes = 3
 batch_size = 32
- # Set the image size and input shape
+# Set the image size and input shape
 img_width, img_height = 100, 100
 input_shape = (img_width, img_height, 1)
- # Load the training and validation data
+# Load the training and validation data
 train_ds = tf.keras.utils.image_dataset_from_directory(
     train_data_dir,
     validation_split=0.2,
@@ -35,15 +34,16 @@ val_ds = tf.keras.utils.image_dataset_from_directory(
     seed=123,
     image_size=(img_height, img_width),
     batch_size=batch_size)
- # Get the class names
+# Get the class names
 class_names = train_ds.class_names
 print(class_names)
- # Normalize the training and validation data
+# Normalize the training and validation data
 train_ds = train_ds.map(normalize)
 val_ds = val_ds.map(normalize)
- # Define the model architecture
+# Define the model architecture
 model = tf.keras.Sequential([
-    layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
+    layers.Conv2D(16, 3, padding='same', activation='relu',
+                  input_shape=(img_height, img_width, 1)),
     layers.MaxPooling2D(),
     layers.Conv2D(32, 3, padding='same', activation='relu'),
     layers.MaxPooling2D(),
@@ -53,16 +53,17 @@ model = tf.keras.Sequential([
     layers.Dense(128, activation='relu'),
     layers.Dense(num_classes, activation='softmax')
 ])
- # Compile the model
+# Compile the model
 model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+              loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                  from_logits=True),
               metrics=['accuracy'])
- # Print the model summary
+# Print the model summary
 model.summary()
- # Train the model
-epochs=10
+# Train the model
+epochs = 10
 model.fit(train_ds,
           validation_data=val_ds,
           epochs=epochs)
- # Save the trained model
+# Save the trained model
 model.save('Network/trained_model.h5')

Network/Predictor.py

@@ -0,0 +1,46 @@
+import os
+import random
+from pathlib import Path
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+
+
+class Predictor:
+    def __init__(self):
+        # Load the trained model
+        self.model = keras.models.load_model('Network/trained_model.h5')
+
+        # Load the class names
+        self.class_names = ['table', 'done', 'order']
+
+        # Path to the folder containing test images
+        self.test_images_folder = 'Network/Testing/'
+
+    def predict(self, image_path):
+        # Load and preprocess the test image
+        test_image = keras.preprocessing.image.load_img(
+            image_path, target_size=(100, 100))
+        test_image = keras.preprocessing.image.img_to_array(test_image)
+        test_image = np.expand_dims(test_image, axis=0)
+        test_image = test_image / 255.0  # Normalize the image
+
+        # Reshape the image array to (1, height, width, channels)
+        test_image = np.reshape(test_image, (1, 100, 100, 3))
+
+        # Make predictions
+        predictions = self.model.predict(test_image)
+        predicted_class_index = np.argmax(predictions[0])
+        predicted_class = self.class_names[predicted_class_index]
+
+        print(predicted_class)
+        return predicted_class
+
+    def random_path_img(self) -> str:
+        folder_name = random.choice(os.listdir(self.test_images_folder))
+        folder_path = os.path.join(self.test_images_folder, folder_name)
+        filename = ""
+        while not (filename.endswith('.jpg') or filename.endswith('.jpeg')):
+            filename = random.choice(os.listdir(folder_path))
+        image_path = os.path.join(folder_path, filename)
+        return image_path

Network/TesterRandom.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
@@ -7,7 +8,7 @@ from tensorflow import keras
 model = keras.models.load_model('Network/trained_model.h5')
 
 # Load the class names
-class_names = ['Table', 'Done','Order']
+class_names = ['table', 'done', 'order']
 
 # Path to the folder containing test images
 test_images_folder = 'Network/Testing/'
@@ -22,25 +23,26 @@ for folder_name in os.listdir(test_images_folder):
 
         # True class based on folder name
         if folder_name == 'Empty':
-            true_class = 'Table'
+            true_class = 'table'
         elif folder_name == 'Food':
-            true_class = 'Done'
+            true_class = 'done'
         elif folder_name == 'People':
-            true_class = 'Order'
+            true_class = 'order'
 
         # Iterate over the files in the subfolder
         for filename in os.listdir(folder_path):
             if filename.endswith('.jpg') or filename.endswith('.jpeg'):
-                i+=1
+                i += 1
                 # Load and preprocess the test image
                 image_path = os.path.join(folder_path, filename)
-                test_image = keras.preprocessing.image.load_img(image_path, target_size=(100, 100))
+                test_image = keras.preprocessing.image.load_img(
+                    image_path, target_size=(100, 100))
                 test_image = keras.preprocessing.image.img_to_array(test_image)
                 test_image = np.expand_dims(test_image, axis=0)
                 test_image = test_image / 255.0  # Normalize the image
 
                 # Reshape the image array to (1, height, width, channels)
-                test_image = np.reshape(test_image, (1,100, 100, 3))
+                test_image = np.reshape(test_image, (1, 100, 100, 3))
 
                 # Make predictions
                 predictions = model.predict(test_image)
@@ -50,7 +52,9 @@ for folder_name in os.listdir(test_images_folder):
                 direct = 'Network/Results/'
                 filename = str(i) + predicted_class + '.jpeg'
                 test_image = np.reshape(test_image, (100, 100, 3))
-                tf.keras.preprocessing.image.save_img(direct+filename, test_image)
+                Path(direct).mkdir(parents=True, exist_ok=True)
+                tf.keras.preprocessing.image.save_img(
+                    direct+filename, test_image)
                 if predicted_class != true_class:
                     errorcount += 1
                 print('Image:', filename)

Network/testerVal.py

@@ -1,4 +1,4 @@
-import os
+from pathlib import Path
 import numpy as np
 import tensorflow as tf
 from tensorflow import keras
@@ -7,7 +7,7 @@ from tensorflow import keras
 model = keras.models.load_model('Network/trained_model.h5')
 
 # Load the class names
-class_names = ['Table', 'Done','Order']
+class_names = ['table', 'done', 'order']
 
 # Load and preprocess the validation dataset
 data_dir = "Network/Training/"
@@ -47,6 +47,7 @@ for i in range(60):
 
     direct = 'Network/Results/'
     filename = predicted_class + str(i) + '.jpeg'
+    Path(direct).mkdir(parents=True, exist_ok=True)
     tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
     if predicted_class != true_class:
         errorcount += 1

README.MD

@@ -49,6 +49,6 @@
 
 ---
 
-- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
-  - [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
-  - [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
+- [x] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
+  - [x] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
+  - [x] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.

requirements.txt

@@ -3,3 +3,5 @@ pygame==2.3.0
 pandas
 scikit-learn
 graphviz
+tensorflow
+pillow

src/obj/Table.py

@@ -1,5 +1,6 @@
 import random
 from src.obj.Object import Object
+from Network.Predictor import Predictor
 
 
 class Table(Object):
@@ -8,6 +9,7 @@ class Table(Object):
         self.waiting_time = 0
         self.cooking_time = 0
         self.is_actual = False
+        self.p = Predictor()
 
     def isActual(self):
         return self.is_actual
@@ -17,7 +19,7 @@ class Table(Object):
             return
         self.is_actual = True
         # here must be neural network choise
-        new_role = random.choice(["table", "order", "wait", "done"])
+        new_role = self.p.predict(self.p.random_path_img())
         self.change_role(new_role, current_time)
 
         if self.agent_role == "table":