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Computer Vision/Celestial_Object_Classification/main.py

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+from src.data_loader import load_data
+from src.model import create_model
+from src.utils import save_training_plots, save_metrics
+import os
+from src.config import *
+
+def main():
+    # Load and preprocess data
+    print("Loading data...")
+    x_train, x_test, y_train, y_test = load_data()
+
+    # Create and train model
+    print("Creating and training model...")
+    model = create_model()
+    history = model.fit(
+        x_train, y_train,
+        epochs=25,
+        validation_data=(x_test, y_test),
+        verbose=1
+    )
+
+    # Save results
+    print("Saving results...")
+    save_training_plots(history)
+    save_metrics(model, x_test, y_test)
+
+    # Save model
+    model.save(os.path.join('results', 'model.h5'))
+    print("Training complete! Results saved in 'results' directory.")
+
+if __name__ == "__main__":
+    main()

Computer Vision/Celestial_Object_Classification/requirements.txt

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+numpy==1.23.5
+scipy==1.10.1
+scikit-learn==1.2.2
+tensorflow==2.12.0
+opencv-python==4.7.0.72
+matplotlib==3.7.1
+seaborn==0.12.2
+pandas==2.0.0

Computer Vision/Celestial_Object_Classification/results/classification_report.txt

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+              precision    recall  f1-score   support
+
+           0       0.72      0.66      0.69       189
+           1       0.90      0.92      0.91       609
+
+    accuracy                           0.86       798
+   macro avg       0.81      0.79      0.80       798
+weighted avg       0.85      0.86      0.85       798

Computer Vision/Celestial_Object_Classification/src/config.py

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+
+import os
+
+# Directory paths
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_DIR = os.path.join(BASE_DIR, 'data')
+RESULTS_DIR = os.path.join(BASE_DIR, 'results')
+
+# Ensure results directory exists
+os.makedirs(RESULTS_DIR, exist_ok=True)
+
+# Model parameters
+IMG_SIZE = 64
+BATCH_SIZE = 32
+EPOCHS = 25
+RANDOM_STATE = 42
+TEST_SIZE = 0.2

Computer Vision/Celestial_Object_Classification/src/data_loader.py

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+import cv2
+import numpy as np
+import glob
+import os
+from sklearn.model_selection import train_test_split
+from .config import *
+
+def load_data():
+    """Load and preprocess image data."""
+    data = []
+    labels = []
+
+    # Load galaxy images
+    galaxy_paths = glob.glob(os.path.join(DATA_DIR, 'galaxy', '*'))
+    for path in galaxy_paths:
+        img = cv2.imread(path, 1)
+        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+        data.append(img)
+        labels.append(0)  # 0 for galaxy
+
+    # Load star images
+    star_paths = glob.glob(os.path.join(DATA_DIR, 'star', '*'))
+    for path in star_paths:
+        img = cv2.imread(path, 1)
+        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
+        data.append(img)
+        labels.append(1)  # 1 for star
+
+    data = np.array(data) / 255.0  # Normalize
+    labels = np.array(labels)
+
+    # Split data
+    x_train, x_test, y_train, y_test = train_test_split(
+        data, labels, 
+        test_size=TEST_SIZE,
+        random_state=RANDOM_STATE,
+        stratify=labels
+    )
+
+    return x_train, x_test, y_train, y_test

Computer Vision/Celestial_Object_Classification/src/model.py

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+from tensorflow.keras import layers, models
+from .config import *
+def create_model():
+    """Create and return the CNN model."""
+    model = models.Sequential([
+        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.MaxPooling2D((2, 2)),
+        layers.Conv2D(64, (3, 3), activation='relu'),
+        layers.Flatten(),
+        layers.Dense(64, activation='relu'),
+        layers.Dense(2, activation='softmax')  # 2 classes: star and galaxy
+    ])
+
+    model.compile(
+        optimizer='adam',
+        loss='sparse_categorical_crossentropy',
+        metrics=['accuracy']
+    )
+
+    return model

Computer Vision/Celestial_Object_Classification/src/utils.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.metrics import confusion_matrix, classification_report, roc_curve, precision_recall_curve
+import numpy as np
+import os
+from .config import RESULTS_DIR
+from .config import *
+
+def save_training_plots(history):
+    """Save accuracy and loss plots."""
+    # Accuracy plot
+    plt.figure(figsize=(10, 5))
+    plt.subplot(1, 2, 1)
+    plt.plot(history.history['accuracy'], label='Training Accuracy')
+    plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
+    plt.title('Model Accuracy')
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend()
+
+    # Loss plot
+    plt.subplot(1, 2, 2)
+    plt.plot(history.history['loss'], label='Training Loss')
+    plt.plot(history.history['val_loss'], label='Validation Loss')
+    plt.title('Model Loss')
+    plt.xlabel('Epoch')
+    plt.ylabel('Loss')
+    plt.legend()
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(RESULTS_DIR, 'training_history.png'))
+    plt.close()
+
+def save_metrics(model, x_test, y_test):
+    """Save confusion matrix, ROC curve, PR curve, and classification report."""
+    # Get predictions
+    y_pred = model.predict(x_test)
+    y_pred_classes = np.argmax(y_pred, axis=1)
+
+    # Confusion Matrix
+    cm = confusion_matrix(y_test, y_pred_classes)
+    plt.figure(figsize=(8, 6))
+    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
+    plt.title('Confusion Matrix')
+    plt.ylabel('True Label')
+    plt.xlabel('Predicted Label')
+    plt.savefig(os.path.join(RESULTS_DIR, 'confusion_matrix.png'))
+    plt.close()
+
+    # ROC Curve
+    fpr, tpr, _ = roc_curve(y_test, y_pred[:, 1])
+    plt.figure(figsize=(8, 6))
+    plt.plot(fpr, tpr)
+    plt.title('ROC Curve')
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.savefig(os.path.join(RESULTS_DIR, 'roc_curve.png'))
+    plt.close()
+
+    # Precision-Recall Curve
+    precision, recall, _ = precision_recall_curve(y_test, y_pred[:, 1])
+    plt.figure(figsize=(8, 6))
+    plt.plot(recall, precision)
+    plt.title('Precision-Recall Curve')
+    plt.xlabel('Recall')
+    plt.ylabel('Precision')
+    plt.savefig(os.path.join(RESULTS_DIR, 'pr_curve.png'))
+    plt.close()
+
+    # Classification Report
+    report = classification_report(y_test, y_pred_classes)
+    with open(os.path.join(RESULTS_DIR, 'classification_report.txt'), 'w') as f:
+        f.write(report)