#!/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"āŒ No data file found at {data_file}") print("šŸ’” Run data collection first: python main.py --mode data --samples 50") return samples = data.get('samples', []) metadata = data.get('metadata', {}) print(f"šŸ“Š MapCrunch Data Collection Summary") print(f"{'='*50}") print(f"šŸ“… Collection Date: {metadata.get('collection_date', 'Unknown')}") print(f"šŸ“ Total Samples: {len(samples)}") print(f"šŸ™ļø Collection Options: {metadata.get('collection_options', {})}") # Statistics stats = metadata.get('statistics', {}) if stats: print(f"\nšŸ“ˆ 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"\nšŸŒ 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"\nšŸ“ 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"šŸ“ø Thumbnail Coverage: {thumbnails_available}/{len(samples)} ({thumbnails_available/len(samples)*100:.1f}%)") # Sample locations print(f"\nšŸ“ 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 = "šŸ“ø" if sample.get('has_thumbnail') else "āŒ" 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 = """ MapCrunch Collection Gallery

MapCrunch Collection Gallery

""" # 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"""

Total Samples: {total} | With Thumbnails: {with_thumb} | With Coordinates: {with_coords}

""" # 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"""
{address}
{address}
{lat}, {lng}
""" count += 1 html += """
""" with open(output_file, 'w') as f: f.write(html) print(f"āœ… Gallery created: {output_file}") print(f"šŸ“ø Included {count} images") print(f"šŸ’” 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("āŒ 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"āœ… 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"āŒ Data file not found: {args.data}") print("šŸ’” 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()