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from PIL import Image
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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from tensorflow.keras.preprocessing.image import load_img, img_to_array
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from sklearn.metrics import confusion_matrix
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def validate_image(file_path):
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"""Validate image integrity
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Some images are corrupted and cannot be opened by PIL
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Args:
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file_path (str): Image file path
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Returns:
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bool: True if image is valid, False otherwise"""
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try:
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with Image.open(file_path) as img:
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img.verify()
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return True
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except Exception as e:
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print(f"Corrupted image: {file_path} - Error: {e}")
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return False
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def visualize_image(train_path, class_name="cat"):
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"""Display image
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Args:
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file_path (str): Image file path
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class_name (str): Image class name
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Returns:
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10 images of the specified class"""
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plt.figure(figsize=(20,20))
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plt.subplots_adjust(hspace=0.4)
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for i in range(10):
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plt.subplot(1,10,i+1)
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filename = train_path +'\\'+ f'{class_name}.' + str(i) + '.jpg'
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image = Image.open(filename)
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plt.imshow(image)
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plt.title(f'{class_name}',fontsize=12)
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plt.axis('off')
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plt.show()
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def visulaize_ouput(history):
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"""Visualize model training output
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Args:
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history (tensorflow.python.keras.callbacks.History): Model training history
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Returns:
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plot of Loss and Accuracy"""
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result_df = pd.DataFrame(history.history)
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plt.figure(figsize=(18,5),dpi=200)
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sns.set_style('darkgrid')
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plt.subplot(121)
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plt.title('Cross Entropy Loss',fontsize=15)
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plt.xlabel('Epochs',fontsize=12)
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plt.ylabel('Loss',fontsize=12)
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plt.plot(result_df['loss'])
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plt.plot(result_df['val_loss'])
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plt.subplot(122)
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plt.title('Classification Accuracy',fontsize=15)
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plt.xlabel('Epochs',fontsize=12)
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plt.ylabel('Accuracy',fontsize=12)
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plt.plot(result_df['accuracy'])
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plt.plot(result_df['val_accuracy'])
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plt.show()
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def predictor(model, image_path, classes, img_size=(128, 128)):
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"""Predict image class
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Args:
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model (tensorflow.python.keras.engine.sequential.Sequential): Model
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image_path (str): Image file path
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classes (list): List of class names
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img_size (tuple): Image size
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Returns:
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Image class prediction with plots"""
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img = load_img(image_path, target_size=img_size)
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img_array = img_to_array(img) / 255.0
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img_array = np.expand_dims(img_array, axis=0)
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predictions = model.predict(img_array)
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predicted_class = np.argmax(predictions, axis=1)[0]
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print(predictions)
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print(predicted_class)
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confidence = predictions[0][predicted_class]
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print(confidence)
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if confidence > 0.5:
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predicted_class = 1
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else:
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predicted_class = 0
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plt.imshow(img)
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plt.axis('off')
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plt.title(f"Prediction: {classes[predicted_class]}")
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plt.show()
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