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| #!/usr/bin/env python3 | |
| """ | |
| Quick viewer for collected MapCrunch data | |
| """ | |
| import json | |
| import os | |
| from pathlib import Path | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| from collections import Counter | |
| def view_data_summary(data_file='data/golden_labels.json'): | |
| """Display summary of collected data""" | |
| try: | |
| with open(data_file, 'r') as f: | |
| data = json.load(f) | |
| except FileNotFoundError: | |
| print(f"❌ No data file found at {data_file}") | |
| print("💡 Run data collection first: python main.py --mode data --samples 50") | |
| return | |
| samples = data.get('samples', []) | |
| metadata = data.get('metadata', {}) | |
| print(f"📊 MapCrunch Data Collection Summary") | |
| print(f"{'='*50}") | |
| print(f"📅 Collection Date: {metadata.get('collection_date', 'Unknown')}") | |
| print(f"📍 Total Samples: {len(samples)}") | |
| print(f"🏙️ Collection Options: {metadata.get('collection_options', {})}") | |
| # Statistics | |
| stats = metadata.get('statistics', {}) | |
| if stats: | |
| print(f"\n📈 Statistics:") | |
| for key, value in stats.items(): | |
| print(f" {key}: {value}") | |
| # Country distribution | |
| countries = [] | |
| for sample in samples: | |
| address = sample.get('address', '') | |
| if address and address != 'Unknown': | |
| # Extract country (usually last part after comma) | |
| country = address.split(', ')[-1].strip() | |
| countries.append(country) | |
| if countries: | |
| country_counts = Counter(countries) | |
| print(f"\n🌍 Top Countries:") | |
| for country, count in country_counts.most_common(10): | |
| print(f" {country}: {count} samples") | |
| # Coordinate coverage | |
| coords_available = sum(1 for s in samples if s.get('lat') is not None) | |
| print(f"\n📍 Coordinate Coverage: {coords_available}/{len(samples)} ({coords_available/len(samples)*100:.1f}%)") | |
| # Thumbnail coverage | |
| thumbnails_available = sum(1 for s in samples if s.get('has_thumbnail')) | |
| print(f"📸 Thumbnail Coverage: {thumbnails_available}/{len(samples)} ({thumbnails_available/len(samples)*100:.1f}%)") | |
| # Sample locations | |
| print(f"\n📍 Sample Locations:") | |
| for i, sample in enumerate(samples[:10]): | |
| address = sample.get('address', 'Unknown') | |
| lat = sample.get('lat', 'N/A') | |
| lng = sample.get('lng', 'N/A') | |
| has_thumb = "📸" if sample.get('has_thumbnail') else "❌" | |
| print(f" {i+1}. {has_thumb} {address} ({lat}, {lng})") | |
| if len(samples) > 10: | |
| print(f" ... and {len(samples) - 10} more") | |
| def create_thumbnail_gallery(data_file='data/golden_labels.json', output_file='data/gallery.html', max_images=100): | |
| """Create an HTML gallery of collected thumbnails""" | |
| with open(data_file, 'r') as f: | |
| data = json.load(f) | |
| samples = data.get('samples', []) | |
| html = """ | |
| <html> | |
| <head> | |
| <title>MapCrunch Collection Gallery</title> | |
| <style> | |
| body { font-family: Arial, sans-serif; background: #f0f0f0; } | |
| h1 { text-align: center; } | |
| .gallery { display: flex; flex-wrap: wrap; justify-content: center; } | |
| .item { | |
| margin: 10px; | |
| background: white; | |
| padding: 10px; | |
| border-radius: 8px; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.1); | |
| text-align: center; | |
| } | |
| .item img { max-width: 320px; border-radius: 4px; } | |
| .address { font-weight: bold; margin: 5px 0; } | |
| .coords { font-size: 0.9em; color: #666; } | |
| .stats { margin: 20px; text-align: center; } | |
| </style> | |
| </head> | |
| <body> | |
| <h1>MapCrunch Collection Gallery</h1> | |
| """ | |
| # Add statistics | |
| total = len(samples) | |
| with_thumb = sum(1 for s in samples if s.get('has_thumbnail')) | |
| with_coords = sum(1 for s in samples if s.get('lat') is not None) | |
| html += f""" | |
| <div class="stats"> | |
| <p>Total Samples: {total} | With Thumbnails: {with_thumb} | With Coordinates: {with_coords}</p> | |
| </div> | |
| <div class="gallery"> | |
| """ | |
| # Add thumbnails | |
| count = 0 | |
| for sample in samples: | |
| if count >= max_images: | |
| break | |
| if sample.get('thumbnail_path'): | |
| thumb_path = f"thumbnails/{sample['thumbnail_path']}" | |
| address = sample.get('address', 'Unknown') | |
| lat = sample.get('lat', 'N/A') | |
| lng = sample.get('lng', 'N/A') | |
| html += f""" | |
| <div class="item"> | |
| <img src="{thumb_path}" alt="{address}"> | |
| <div class="address">{address}</div> | |
| <div class="coords">{lat}, {lng}</div> | |
| </div> | |
| """ | |
| count += 1 | |
| html += """ | |
| </div> | |
| </body> | |
| </html> | |
| """ | |
| with open(output_file, 'w') as f: | |
| f.write(html) | |
| print(f"✅ Gallery created: {output_file}") | |
| print(f"📸 Included {count} images") | |
| print(f"💡 Open in browser: file://{os.path.abspath(output_file)}") | |
| def plot_thumbnails_grid(data_file='data/golden_labels.json', max_images=20): | |
| """Display a grid of thumbnails using matplotlib""" | |
| with open(data_file, 'r') as f: | |
| data = json.load(f) | |
| samples = [s for s in data['samples'] if s.get('thumbnail_path')][:max_images] | |
| if not samples: | |
| print("❌ No samples with thumbnails found") | |
| return | |
| # Create grid | |
| cols = 5 | |
| rows = (len(samples) + cols - 1) // cols | |
| fig, axes = plt.subplots(rows, cols, figsize=(15, rows * 3)) | |
| if rows == 1: | |
| axes = axes.reshape(1, -1) | |
| for i, sample in enumerate(samples): | |
| row = i // cols | |
| col = i % cols | |
| thumb_path = f"data/thumbnails/{sample['thumbnail_path']}" | |
| if os.path.exists(thumb_path): | |
| img = mpimg.imread(thumb_path) | |
| axes[row, col].imshow(img) | |
| axes[row, col].set_title(sample.get('address', 'Unknown')[:30] + '...', fontsize=8) | |
| axes[row, col].axis('off') | |
| # Hide empty subplots | |
| for i in range(len(samples), rows * cols): | |
| row = i // cols | |
| col = i % cols | |
| axes[row, col].axis('off') | |
| plt.tight_layout() | |
| plt.suptitle(f'MapCrunch Collection Sample ({len(samples)} locations)', y=1.02) | |
| plt.show() | |
| def export_coordinates_csv(data_file='data/golden_labels.json', output_file='data/coordinates.csv'): | |
| """Export coordinates to CSV for mapping""" | |
| import csv | |
| with open(data_file, 'r') as f: | |
| data = json.load(f) | |
| samples = data.get('samples', []) | |
| with open(output_file, 'w', newline='', encoding='utf-8') as f: | |
| writer = csv.writer(f) | |
| writer.writerow(['id', 'address', 'latitude', 'longitude', 'has_thumbnail']) | |
| count = 0 | |
| for sample in samples: | |
| if sample.get('lat') is not None and sample.get('lng') is not None: | |
| writer.writerow([ | |
| sample['id'][:8], | |
| sample.get('address', 'Unknown'), | |
| sample['lat'], | |
| sample['lng'], | |
| 'Yes' if sample.get('has_thumbnail') else 'No' | |
| ]) | |
| count += 1 | |
| print(f"✅ Exported {count} coordinates to {output_file}") | |
| def main(): | |
| import argparse | |
| parser = argparse.ArgumentParser(description='View collected MapCrunch data') | |
| parser.add_argument('--gallery', action='store_true', help='Create HTML gallery') | |
| parser.add_argument('--grid', action='store_true', help='Show thumbnail grid') | |
| parser.add_argument('--csv', action='store_true', help='Export coordinates to CSV') | |
| parser.add_argument('--data', default='data/golden_labels.json', help='Data file path') | |
| parser.add_argument('--max-images', type=int, default=50, help='Max images for gallery/grid') | |
| args = parser.parse_args() | |
| if not os.path.exists(args.data): | |
| print(f"❌ Data file not found: {args.data}") | |
| print("💡 Run data collection first: python main.py --mode data --samples 50") | |
| return | |
| # Always show summary | |
| view_data_summary(args.data) | |
| # Additional actions | |
| if args.gallery: | |
| print() | |
| create_thumbnail_gallery(args.data, max_images=args.max_images) | |
| if args.grid: | |
| print() | |
| plot_thumbnails_grid(args.data, max_images=args.max_images) | |
| if args.csv: | |
| print() | |
| export_coordinates_csv(args.data) | |
| if __name__ == "__main__": | |
| main() |