refactoring

Network/Network.py

@@ -1,69 +0,0 @@
-import tensorflow as tf
-from keras import layers
-
-# 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
-num_classes = 3
-batch_size = 32
-# 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
-train_ds = tf.keras.utils.image_dataset_from_directory(
-    train_data_dir,
-    validation_split=0.2,
-    subset="training",
-    shuffle=True,
-    seed=123,
-    image_size=(img_height, img_width),
-    batch_size=batch_size)
-
-val_ds = tf.keras.utils.image_dataset_from_directory(
-    train_data_dir,
-    validation_split=0.2,
-    subset="validation",
-    shuffle=True,
-    seed=123,
-    image_size=(img_height, img_width),
-    batch_size=batch_size)
-# Get the class names
-class_names = train_ds.class_names
-print(class_names)
-# Normalize the training and validation data
-train_ds = train_ds.map(normalize)
-val_ds = val_ds.map(normalize)
-# Define the model architecture
-model = tf.keras.Sequential([
-    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(),
-    layers.Conv2D(64, 3, padding='same', activation='relu'),
-    layers.MaxPooling2D(),
-    layers.Flatten(),
-    layers.Dense(128, activation='relu'),
-    layers.Dense(num_classes, activation='softmax')
-])
-# Compile the model
-model.compile(optimizer='adam',
-              loss=tf.keras.losses.SparseCategoricalCrossentropy(
-                  from_logits=True),
-              metrics=['accuracy'])
-# Print the model summary
-model.summary()
-# Train the model
-epochs = 10
-model.fit(train_ds,
-          validation_data=val_ds,
-          epochs=epochs)
-# Save the trained model
-model.save('Network/trained_model.h5')

Network/Predictor.py

@@ -1,50 +0,0 @@
-import os
-import random
-import logging
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-from termcolor import colored
-
-
-class Predictor:
-    def __init__(self):
-        # Turn off interactive logging
-        tf.get_logger().setLevel(logging.ERROR)
-
-        # Load the trained model
-        self.model = keras.models.load_model('Network/trained_model.h5')
-
-        # Load the class names
-        self.class_names = ['table', 'table', '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, verbose=None)
-        predicted_class_index = np.argmax(predictions[0])
-        predicted_class = self.class_names[predicted_class_index]
-
-        print(colored("Predicted class: ", "yellow")+f"{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

@@ -1,64 +0,0 @@
-import os
-from pathlib import Path
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-
-# Load the trained model
-model = keras.models.load_model('Network/trained_model.h5')
-
-# Load the class names
-class_names = ['table', 'done', 'order']
-
-# Path to the folder containing test images
-test_images_folder = 'Network/Testing/'
-
-# Iterate over the test images
-i = 0
-errorcount = 0
-for folder_name in os.listdir(test_images_folder):
-    folder_path = os.path.join(test_images_folder, folder_name)
-    if os.path.isdir(folder_path):
-        print('Testing images in folder:', folder_name)
-
-        # True class based on folder name
-        if folder_name == 'Empty':
-            true_class = 'table'
-        elif folder_name == 'Food':
-            true_class = 'done'
-        elif folder_name == 'People':
-            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
-                # 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.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 = model.predict(test_image)
-                predicted_class_index = np.argmax(predictions[0])
-                predicted_class = class_names[predicted_class_index]
-
-                direct = 'Network/Results/'
-                filename = str(i) + predicted_class + '.jpeg'
-                test_image = np.reshape(test_image, (100, 100, 3))
-                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)
-                print('True class:', true_class)
-                print('Predicted class:', predicted_class)
-                print()
-print('Error count: ', errorcount)

Network/testerVal.py

@@ -1,58 +0,0 @@
-from pathlib import Path
-import numpy as np
-import tensorflow as tf
-from tensorflow import keras
-
-# Load the trained model
-model = keras.models.load_model('Network/trained_model.h5')
-
-# Load the class names
-class_names = ['table', 'done', 'order']
-
-# Load and preprocess the validation dataset
-data_dir = "Network/Training/"
-image_size = (100, 100)
-batch_size = 32
-
-val_ds = tf.keras.preprocessing.image_dataset_from_directory(
-    data_dir,
-    validation_split=0.2,
-    subset="validation",
-    seed=123,
-    image_size=image_size,
-    batch_size=batch_size,
-)
-
-# Select 20 random images from the validation set
-val_images = []
-val_labels = []
-for images, labels in val_ds.unbatch().shuffle(1000).take(60):
-    val_images.append(images)
-    val_labels.append(labels)
-
-# Make predictions on the random images
-errorcount = 0
-for i in range(60):
-    test_image = val_images[i]
-    test_label = val_labels[i]
-    test_image = np.expand_dims(test_image, axis=0)
-    test_image = test_image / 255.0  # Normalize the image
-
-    # Make predictions
-    predictions = model.predict(test_image)
-    predicted_class_index = np.argmax(predictions[0])
-    predicted_class = class_names[predicted_class_index]
-
-    true_class = class_names[test_label]
-
-    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
-    print('Image', i+1)
-    print('True class:', true_class)
-    print('Predicted class:', predicted_class)
-    print()
-print('Error count: ', errorcount)

agent.py

@@ -1,14 +1,9 @@
-import random
-from termcolor import colored
 from src.Engine import Engine
 from src.obj.Waiter import Waiter
 from src.obj.Kitchen import Kitchen
 from src.controller.LayoutController import LayoutController
-
 from src.controller.ImageController import ImageController
 
-print(colored("Initialization...", "green"))
-
 SCREEN_SIZE = [800, 800]
 SQUARE_SIZE = 80
 SLEEP_DURATION = 0.125
@@ -20,6 +15,4 @@ kitchen = Kitchen([0, 0], 0, SQUARE_SIZE, SCREEN_SIZE, store)
 engine = Engine(SCREEN_SIZE, SQUARE_SIZE, kitchen, waiter, SLEEP_DURATION)
 layout = LayoutController(engine, store).create_and_subscribe(COUNT_OF_OBJECTS)
 
-print(colored("Starting model...", "green"))
-
 engine.loop()