neural network integration
This commit is contained in:
parent
56ca4bc891
commit
b26f7b244e
@ -1,49 +1,49 @@
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import tensorflow as tf
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import tensorflow as tf
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from keras import layers
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from keras import layers
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from keras.models import Sequential
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from keras.optimizers import Adam
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from keras.utils import to_categorical
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from keras.preprocessing.image import ImageDataGenerator
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# Normalizes the pixel values of an image to the range [0, 1].
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# Normalizes the pixel values of an image to the range [0, 1].
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def normalize(image, label):
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def normalize(image, label):
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return image / 255, label
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return image / 255, label
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# Set the paths to the folder containing the training data
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# Set the paths to the folder containing the training data
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train_data_dir = "Network/Training/"
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train_data_dir = "Network/Training/"
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# Set the number of classes and batch size
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# Set the number of classes and batch size
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num_classes = 3
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num_classes = 3
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batch_size = 32
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batch_size = 32
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# Set the image size and input shape
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# Set the image size and input shape
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img_width, img_height = 100, 100
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img_width, img_height = 100, 100
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input_shape = (img_width, img_height, 1)
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input_shape = (img_width, img_height, 1)
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# Load the training and validation data
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# Load the training and validation data
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train_ds = tf.keras.utils.image_dataset_from_directory(
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train_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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train_data_dir,
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validation_split=0.2,
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validation_split=0.2,
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subset="training",
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subset="training",
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shuffle=True,
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shuffle=True,
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seed=123,
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seed=123,
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image_size=(img_height, img_width),
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image_size=(img_height, img_width),
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batch_size=batch_size)
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batch_size=batch_size)
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val_ds = tf.keras.utils.image_dataset_from_directory(
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val_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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train_data_dir,
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validation_split=0.2,
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validation_split=0.2,
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subset="validation",
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subset="validation",
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shuffle=True,
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shuffle=True,
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seed=123,
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seed=123,
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image_size=(img_height, img_width),
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image_size=(img_height, img_width),
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batch_size=batch_size)
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batch_size=batch_size)
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# Get the class names
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# Get the class names
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class_names = train_ds.class_names
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class_names = train_ds.class_names
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print(class_names)
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print(class_names)
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# Normalize the training and validation data
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# Normalize the training and validation data
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train_ds = train_ds.map(normalize)
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train_ds = train_ds.map(normalize)
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val_ds = val_ds.map(normalize)
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val_ds = val_ds.map(normalize)
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# Define the model architecture
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# Define the model architecture
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model = tf.keras.Sequential([
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model = tf.keras.Sequential([
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layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
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layers.Conv2D(16, 3, padding='same', activation='relu',
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input_shape=(img_height, img_width, 1)),
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layers.MaxPooling2D(),
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layers.MaxPooling2D(),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.MaxPooling2D(),
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@ -53,16 +53,17 @@ model = tf.keras.Sequential([
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layers.Dense(128, activation='relu'),
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layers.Dense(128, activation='relu'),
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layers.Dense(num_classes, activation='softmax')
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layers.Dense(num_classes, activation='softmax')
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])
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])
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# Compile the model
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# Compile the model
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model.compile(optimizer='adam',
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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loss=tf.keras.losses.SparseCategoricalCrossentropy(
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from_logits=True),
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metrics=['accuracy'])
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metrics=['accuracy'])
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# Print the model summary
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# Print the model summary
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model.summary()
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model.summary()
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# Train the model
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# Train the model
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epochs=10
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epochs = 10
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model.fit(train_ds,
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model.fit(train_ds,
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validation_data=val_ds,
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validation_data=val_ds,
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epochs=epochs)
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epochs=epochs)
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# Save the trained model
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# Save the trained model
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model.save('Network/trained_model.h5')
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model.save('Network/trained_model.h5')
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46
Network/Predictor.py
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46
Network/Predictor.py
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import os
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import random
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from pathlib import Path
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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class Predictor:
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def __init__(self):
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# Load the trained model
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self.model = keras.models.load_model('Network/trained_model.h5')
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# Load the class names
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self.class_names = ['table', 'done', 'order']
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# Path to the folder containing test images
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self.test_images_folder = 'Network/Testing/'
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def predict(self, image_path):
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# Load and preprocess the test image
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test_image = keras.preprocessing.image.load_img(
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image_path, target_size=(100, 100))
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test_image = keras.preprocessing.image.img_to_array(test_image)
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test_image = np.expand_dims(test_image, axis=0)
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test_image = test_image / 255.0 # Normalize the image
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# Reshape the image array to (1, height, width, channels)
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test_image = np.reshape(test_image, (1, 100, 100, 3))
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# Make predictions
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predictions = self.model.predict(test_image)
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predicted_class_index = np.argmax(predictions[0])
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predicted_class = self.class_names[predicted_class_index]
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print(predicted_class)
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return predicted_class
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def random_path_img(self) -> str:
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folder_name = random.choice(os.listdir(self.test_images_folder))
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folder_path = os.path.join(self.test_images_folder, folder_name)
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filename = ""
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while not (filename.endswith('.jpg') or filename.endswith('.jpeg')):
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filename = random.choice(os.listdir(folder_path))
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image_path = os.path.join(folder_path, filename)
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return image_path
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@ -1,4 +1,5 @@
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import os
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import os
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from pathlib import Path
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import numpy as np
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import numpy as np
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import tensorflow as tf
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow import keras
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model = keras.models.load_model('Network/trained_model.h5')
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model = keras.models.load_model('Network/trained_model.h5')
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# Load the class names
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# Load the class names
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class_names = ['Table', 'Done','Order']
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class_names = ['table', 'done', 'order']
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# Path to the folder containing test images
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# Path to the folder containing test images
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test_images_folder = 'Network/Testing/'
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test_images_folder = 'Network/Testing/'
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@ -19,42 +20,45 @@ for folder_name in os.listdir(test_images_folder):
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folder_path = os.path.join(test_images_folder, folder_name)
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folder_path = os.path.join(test_images_folder, folder_name)
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if os.path.isdir(folder_path):
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if os.path.isdir(folder_path):
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print('Testing images in folder:', folder_name)
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print('Testing images in folder:', folder_name)
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# True class based on folder name
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# True class based on folder name
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if folder_name == 'Empty':
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if folder_name == 'Empty':
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true_class = 'Table'
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true_class = 'table'
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elif folder_name == 'Food':
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elif folder_name == 'Food':
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true_class = 'Done'
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true_class = 'done'
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elif folder_name == 'People':
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elif folder_name == 'People':
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true_class = 'Order'
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true_class = 'order'
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# Iterate over the files in the subfolder
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# Iterate over the files in the subfolder
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for filename in os.listdir(folder_path):
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for filename in os.listdir(folder_path):
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if filename.endswith('.jpg') or filename.endswith('.jpeg'):
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if filename.endswith('.jpg') or filename.endswith('.jpeg'):
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i+=1
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i += 1
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# Load and preprocess the test image
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# Load and preprocess the test image
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image_path = os.path.join(folder_path, filename)
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image_path = os.path.join(folder_path, filename)
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test_image = keras.preprocessing.image.load_img(image_path, target_size=(100, 100))
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test_image = keras.preprocessing.image.load_img(
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image_path, target_size=(100, 100))
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test_image = keras.preprocessing.image.img_to_array(test_image)
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test_image = keras.preprocessing.image.img_to_array(test_image)
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test_image = np.expand_dims(test_image, axis=0)
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test_image = np.expand_dims(test_image, axis=0)
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test_image = test_image / 255.0 # Normalize the image
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test_image = test_image / 255.0 # Normalize the image
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# Reshape the image array to (1, height, width, channels)
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# Reshape the image array to (1, height, width, channels)
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test_image = np.reshape(test_image, (1,100, 100, 3))
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test_image = np.reshape(test_image, (1, 100, 100, 3))
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# Make predictions
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# Make predictions
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predictions = model.predict(test_image)
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predictions = model.predict(test_image)
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predicted_class_index = np.argmax(predictions[0])
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predicted_class_index = np.argmax(predictions[0])
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predicted_class = class_names[predicted_class_index]
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predicted_class = class_names[predicted_class_index]
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direct = 'Network/Results/'
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direct = 'Network/Results/'
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filename = str(i) + predicted_class + '.jpeg'
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filename = str(i) + predicted_class + '.jpeg'
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test_image = np.reshape(test_image, (100, 100, 3))
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test_image = np.reshape(test_image, (100, 100, 3))
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tf.keras.preprocessing.image.save_img(direct+filename, test_image)
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Path(direct).mkdir(parents=True, exist_ok=True)
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tf.keras.preprocessing.image.save_img(
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direct+filename, test_image)
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if predicted_class != true_class:
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if predicted_class != true_class:
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errorcount += 1
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errorcount += 1
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print('Image:', filename)
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print('Image:', filename)
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print('True class:', true_class)
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print('True class:', true_class)
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print('Predicted class:', predicted_class)
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print('Predicted class:', predicted_class)
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print()
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print()
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print('Error count: ', errorcount)
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print('Error count: ', errorcount)
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import os
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from pathlib import Path
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import numpy as np
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import numpy as np
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import tensorflow as tf
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow import keras
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model = keras.models.load_model('Network/trained_model.h5')
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model = keras.models.load_model('Network/trained_model.h5')
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# Load the class names
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# Load the class names
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class_names = ['Table', 'Done','Order']
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class_names = ['table', 'done', 'order']
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# Load and preprocess the validation dataset
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# Load and preprocess the validation dataset
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data_dir = "Network/Training/"
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data_dir = "Network/Training/"
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@ -47,6 +47,7 @@ for i in range(60):
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direct = 'Network/Results/'
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direct = 'Network/Results/'
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filename = predicted_class + str(i) + '.jpeg'
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filename = predicted_class + str(i) + '.jpeg'
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Path(direct).mkdir(parents=True, exist_ok=True)
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tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
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tf.keras.preprocessing.image.save_img(direct+filename, val_images[i])
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if predicted_class != true_class:
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if predicted_class != true_class:
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errorcount += 1
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errorcount += 1
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@ -54,4 +55,4 @@ for i in range(60):
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print('True class:', true_class)
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print('True class:', true_class)
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print('Predicted class:', predicted_class)
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print('Predicted class:', predicted_class)
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print()
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print()
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print('Error count: ', errorcount)
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print('Error count: ', errorcount)
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@ -49,6 +49,6 @@
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---
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---
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- [ ] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
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- [x] **Sieci neuronowe: wymagania dot. czwartego przyrostu**
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- [ ] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
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- [x] Należy przygotować zbiór uczący zawierający co najmniej 1000 przykładów dla każdej klasy.
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- [ ] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
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- [x] Agent powinien wykorzystywać wyuczoną sieć w procesie podejmowania decyzji.
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@ -2,4 +2,6 @@
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pygame==2.3.0
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pygame==2.3.0
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pandas
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pandas
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scikit-learn
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scikit-learn
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graphviz
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graphviz
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tensorflow
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pillow
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import random
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import random
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from src.obj.Object import Object
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from src.obj.Object import Object
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from Network.Predictor import Predictor
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class Table(Object):
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class Table(Object):
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@ -8,6 +9,7 @@ class Table(Object):
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self.waiting_time = 0
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self.waiting_time = 0
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self.cooking_time = 0
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self.cooking_time = 0
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self.is_actual = False
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self.is_actual = False
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self.p = Predictor()
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def isActual(self):
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def isActual(self):
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return self.is_actual
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return self.is_actual
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return
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return
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self.is_actual = True
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self.is_actual = True
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# here must be neural network choise
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# here must be neural network choise
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new_role = random.choice(["table", "order", "wait", "done"])
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new_role = self.p.predict(self.p.random_path_img())
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self.change_role(new_role, current_time)
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self.change_role(new_role, current_time)
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if self.agent_role == "table":
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if self.agent_role == "table":
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