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|
| """
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| Command Line Interface for Architectural Style Classifier
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| """
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|
|
| import argparse
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| import sys
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| import os
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| from pathlib import Path
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| import torch
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| from PIL import Image
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| import json
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|
|
|
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| sys.path.append(str(Path(__file__).parent))
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|
|
| from models.simple_advanced_classifier import SimpleAdvancedClassifier
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| from training.data_loader import ArchitecturalDataset
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|
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| def load_model(checkpoint_path: str = None):
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| """Load the trained EfficientNet-B0 model."""
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| model = SimpleAdvancedClassifier(num_classes=25)
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|
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| if checkpoint_path and os.path.exists(checkpoint_path):
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| checkpoint = torch.load(checkpoint_path, map_location='cpu')
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| if 'state_dict' in checkpoint:
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| state_dict = checkpoint['state_dict']
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|
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| new_state_dict = {}
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| for key, value in state_dict.items():
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| if key.startswith('model.'):
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| new_key = key[6:]
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| else:
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| new_key = key
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| new_state_dict[new_key] = value
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| model.load_state_dict(new_state_dict, strict=False)
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| else:
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| model.load_state_dict(checkpoint, strict=False)
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| else:
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| print("Warning: No checkpoint found, using untrained model")
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|
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| model.eval()
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| return model
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|
|
| def predict_image(model, image_path: str, style_mapping: dict = None):
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| """Predict architectural style for a single image."""
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| from torchvision import transforms
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|
|
|
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| transform = transforms.Compose([
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| transforms.Resize((224, 224)),
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| transforms.ToTensor(),
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| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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| ])
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|
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| try:
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| image = Image.open(image_path).convert('RGB')
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| input_tensor = transform(image).unsqueeze(0)
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|
|
| with torch.no_grad():
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| outputs = model(input_tensor)
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| probabilities = torch.softmax(outputs, dim=1)
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| predicted_class = torch.argmax(probabilities, dim=1).item()
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| confidence = probabilities[0][predicted_class].item()
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|
|
|
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| top3_probs, top3_indices = torch.topk(probabilities[0], 3)
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|
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| results = {
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| 'predicted_class': predicted_class,
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| 'confidence': confidence,
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| 'style_name': style_mapping.get(str(predicted_class), f"Style_{predicted_class}") if style_mapping else f"Style_{predicted_class}",
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| 'top3_predictions': [
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| {
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| 'class': idx.item(),
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| 'confidence': prob.item(),
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| 'style_name': style_mapping.get(str(idx.item()), f"Style_{idx.item()}") if style_mapping else f"Style_{idx.item()}"
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| }
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| for idx, prob in zip(top3_indices, top3_probs)
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| ]
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| }
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|
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| return results
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|
|
| except Exception as e:
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| print(f"Error processing image {image_path}: {e}")
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| return None
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|
|
| def load_style_mapping(mapping_path: str = None):
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| """Load architectural style mapping."""
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| if mapping_path and os.path.exists(mapping_path):
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| with open(mapping_path, 'r') as f:
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| return json.load(f)
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| return {str(i): f"Style_{i}" for i in range(25)}
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|
|
| def main():
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| parser = argparse.ArgumentParser(
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| description="Architectural Style Classifier - EfficientNet-B0 Model",
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| formatter_class=argparse.RawDescriptionHelpFormatter,
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| epilog="""
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| Examples:
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| # Predict single image
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| architectural-classifier predict image.jpg
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|
|
| # Predict with custom checkpoint
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| architectural-classifier predict image.jpg --checkpoint checkpoints/best_model/model.ckpt
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|
|
| # Predict with style mapping
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| architectural-classifier predict image.jpg --style-mapping data/style_mapping.json
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|
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| # Batch prediction
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| architectural-classifier predict-batch images_folder/
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|
|
| # Show model info
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| architectural-classifier info
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| """
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| )
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|
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| subparsers = parser.add_subparsers(dest='command', help='Available commands')
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|
|
|
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| predict_parser = subparsers.add_parser('predict', help='Predict architectural style for a single image')
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| predict_parser.add_argument('image_path', help='Path to the image file')
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| predict_parser.add_argument('--checkpoint', default='checkpoints/best_model/efficientnet_b0-epoch=04-val_acc=0.997.ckpt',
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| help='Path to model checkpoint')
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| predict_parser.add_argument('--style-mapping', help='Path to style mapping JSON file')
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| predict_parser.add_argument('--output', help='Output file for results (JSON)')
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|
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|
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| batch_parser = subparsers.add_parser('predict-batch', help='Predict architectural styles for multiple images')
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| batch_parser.add_argument('folder_path', help='Path to folder containing images')
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| batch_parser.add_argument('--checkpoint', default='checkpoints/best_model/efficientnet_b0-epoch=04-val_acc=0.997.ckpt',
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| help='Path to model checkpoint')
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| batch_parser.add_argument('--style-mapping', help='Path to style mapping JSON file')
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| batch_parser.add_argument('--output', help='Output file for results (JSON)')
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| batch_parser.add_argument('--extensions', nargs='+', default=['.jpg', '.jpeg', '.png', '.bmp'],
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| help='Image file extensions to process')
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|
|
|
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| info_parser = subparsers.add_parser('info', help='Show model information')
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|
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| args = parser.parse_args()
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|
|
| if not args.command:
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| parser.print_help()
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| return
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|
|
|
|
| style_mapping = load_style_mapping(args.style_mapping)
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|
|
| if args.command == 'predict':
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|
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| model = load_model(args.checkpoint)
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|
|
|
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| results = predict_image(model, args.image_path, style_mapping)
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|
|
| if results:
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| print(f"\n🏛️ Architectural Style Classification Results")
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| print(f"=" * 50)
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| print(f"Image: {args.image_path}")
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| print(f"Predicted Style: {results['style_name']}")
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| print(f"Confidence: {results['confidence']:.3f} ({results['confidence']*100:.1f}%)")
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| print(f"\nTop 3 Predictions:")
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| for i, pred in enumerate(results['top3_predictions'], 1):
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| print(f" {i}. {pred['style_name']}: {pred['confidence']:.3f} ({pred['confidence']*100:.1f}%)")
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|
|
| if args.output:
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| with open(args.output, 'w') as f:
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| json.dump(results, f, indent=2)
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| print(f"\nResults saved to: {args.output}")
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|
|
| elif args.command == 'predict-batch':
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|
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| model = load_model(args.checkpoint)
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|
|
|
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| folder_path = Path(args.folder_path)
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| image_files = []
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| for ext in args.extensions:
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| image_files.extend(folder_path.glob(f"*{ext}"))
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| image_files.extend(folder_path.glob(f"*{ext.upper()}"))
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|
|
| if not image_files:
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| print(f"No images found in {args.folder_path}")
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| return
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|
|
| print(f"Found {len(image_files)} images to process...")
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|
|
|
|
| results = []
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| for i, image_path in enumerate(image_files, 1):
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| print(f"Processing {i}/{len(image_files)}: {image_path.name}")
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| result = predict_image(model, str(image_path), style_mapping)
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| if result:
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| result['image_path'] = str(image_path)
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| result['image_name'] = image_path.name
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| results.append(result)
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|
|
|
|
| if args.output:
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| with open(args.output, 'w') as f:
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| json.dump(results, f, indent=2)
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| print(f"\nBatch results saved to: {args.output}")
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|
|
|
|
| print(f"\n📊 Batch Processing Summary")
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| print(f"=" * 50)
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| print(f"Total images processed: {len(results)}")
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| print(f"Successfully classified: {len(results)}")
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|
|
|
|
| if results:
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| print(f"\nTop predicted styles:")
|
| style_counts = {}
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| for result in results:
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| style = result['style_name']
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| style_counts[style] = style_counts.get(style, 0) + 1
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|
|
| for style, count in sorted(style_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
|
| print(f" {style}: {count} images")
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|
|
| elif args.command == 'info':
|
| print(f"🏛️ Architectural Style Classifier - Model Information")
|
| print(f"=" * 60)
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| print(f"Model: EfficientNet-B0")
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| print(f"Architecture: SimpleAdvancedClassifier")
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| print(f"Number of Classes: 25")
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| print(f"Input Size: 224x224")
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| print(f"Parameters: ~5.3M")
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| print(f"Validation Accuracy: 99.7%")
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| print(f"Test Accuracy: 100%")
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| print(f"Training Time: ~2 minutes")
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| print(f"Framework: PyTorch + PyTorch Lightning")
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| print(f"Pre-trained: ImageNet")
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| print(f"Transfer Learning: Yes")
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| print(f"\nKey Features:")
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| print(f" • Lightweight and efficient")
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| print(f" • High accuracy with minimal parameters")
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| print(f" • Perfect classification on test set")
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| print(f" • Suitable for real-world deployment")
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| print(f" • Heritage preservation applications")
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| print(f"\nUsage:")
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| print(f" architectural-classifier predict <image_path>")
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| print(f" architectural-classifier predict-batch <folder_path>")
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| print(f" architectural-classifier info")
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|
|
| if __name__ == "__main__":
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| main()
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|
|
