Upload generate_kolam_demo.py

generate_kolam_demo.py

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+"""
+Simple script to generate a Kolam image using the AI generator.
+"""
+
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+import sys
+
+# Add project paths
+sys.path.insert(0, str(Path.cwd()))
+sys.path.insert(0, str(Path.cwd() / 'models'))
+
+from models.gan_generator import KolamGenerator
+from utils.image_utils import create_synthetic_kolam
+from utils.metrics import KolamDesignMetrics
+
+def generate_ai_kolam():
+    """Generate a Kolam image using the AI generator."""
+    print("🎨 Generating AI Kolam Design...")
+    
+    # Create the generator
+    generator = KolamGenerator(
+        noise_dim=100,
+        feature_dim=128, 
+        output_channels=1,
+        image_size=64
+    )
+    generator.eval()
+    
+    # Generate random noise
+    noise = torch.randn(1, 100)
+    
+    # Generate the Kolam
+    with torch.no_grad():
+        generated_kolam = generator(noise)
+    
+    # Convert to numpy and normalize
+    kolam_image = generated_kolam.squeeze().cpu().numpy()
+    kolam_image = (kolam_image + 1) / 2  # Convert from [-1, 1] to [0, 1]
+    kolam_image = np.clip(kolam_image, 0, 1)
+    
+    return kolam_image
+
+def generate_synthetic_kolam():
+    """Generate a synthetic Kolam using traditional patterns."""
+    print("🎨 Generating Synthetic Kolam Design...")
+    
+    # Create a synthetic Kolam with medium complexity
+    kolam_image = create_synthetic_kolam(size=(64, 64), complexity='medium')
+    
+    return kolam_image
+
+def analyze_kolam_quality(kolam_image):
+    """Analyze the quality of the generated Kolam."""
+    print("📏 Analyzing Kolam Quality...")
+    
+    metrics = KolamDesignMetrics()
+    quality = metrics.calculate_overall_quality(kolam_image)
+    
+    print(f"Overall Quality Score: {quality['overall_quality']:.3f}")
+    print(f"Horizontal Symmetry: {quality['horizontal']:.3f}")
+    print(f"Vertical Symmetry: {quality['vertical']:.3f}")
+    print(f"Complexity: {quality['complexity']:.3f}")
+    print(f"Balance: {quality['balance']:.3f}")
+    print(f"Rhythm: {quality['rhythm']:.3f}")
+    
+    return quality
+
+def display_kolam(kolam_image, title="Generated Kolam Design"):
+    """Display the Kolam image."""
+    plt.figure(figsize=(8, 8))
+    plt.imshow(kolam_image, cmap='gray', vmin=0, vmax=1)
+    plt.title(title, fontsize=16, fontweight='bold')
+    plt.axis('off')
+    
+    # Save the image
+    output_dir = Path('data/generated')
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    plt.savefig(output_dir / 'generated_kolam.png', dpi=300, bbox_inches='tight')
+    print(f"✅ Kolam saved to: {output_dir / 'generated_kolam.png'}")
+    
+    plt.show()
+
+def main():
+    """Main function to generate and display Kolam."""
+    print("🚀 KOLAM AI GENERATOR - IMAGE GENERATION")
+    print("=" * 50)
+    
+    # Generate AI Kolam
+    ai_kolam = generate_ai_kolam()
+    print("✅ AI Kolam generated!")
+    
+    # Analyze quality
+    quality = analyze_kolam_quality(ai_kolam)
+    
+    # Display the Kolam
+    display_kolam(ai_kolam, "AI Generated Kolam Design")
+    
+    print("\n🎉 Kolam generation complete!")
+    print("\nTo generate more Kolams:")
+    print("1. Run this script again: python generate_kolam_demo.py")
+    print("2. Use the Jupyter notebooks for interactive generation")
+    print("3. Train the models with real data for better results")
+
+if __name__ == "__main__":
+    main()