| 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):
|
| """Validate image integrity
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| Some images are corrupted and cannot be opened by PIL
|
| 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"""
|
|
|
| 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"):
|
| """Display image
|
| Args:
|
| 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"""
|
|
|
| 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):
|
| """Visualize model training output
|
| Args:
|
| history (tensorflow.python.keras.callbacks.History): Model training history
|
| Returns:
|
| plot of Loss and Accuracy"""
|
|
|
| result_df = pd.DataFrame(history.history)
|
|
|
| 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'])
|
|
|
| plt.show()
|
|
|
|
|
| def predictor(model, image_path, classes, img_size=(128, 128)):
|
| """Predict image class
|
| Args:
|
| model (tensorflow.python.keras.engine.sequential.Sequential): Model
|
| image_path (str): Image file path
|
| classes (list): List of class names
|
| img_size (tuple): Image size
|
| Returns:
|
| Image class prediction with plots"""
|
|
|
| img = load_img(image_path, target_size=img_size)
|
| img_array = img_to_array(img) / 255.0
|
| 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]
|
| print(predictions)
|
| print(predicted_class)
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| confidence = predictions[0][predicted_class]
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| print(confidence)
|
| if confidence > 0.5:
|
| predicted_class = 1
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| else:
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| predicted_class = 0
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|
|
| plt.imshow(img)
|
| plt.axis('off')
|
| plt.title(f"Prediction: {classes[predicted_class]}")
|
| plt.show()
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|
|
|
|
