demo.pyā¢6.64 kB
#!/usr/bin/env python3
"""
Demo script showing the Enhanced Image Analysis MCP Server capabilities
"""
import sys
import os
sys.path.append('/Users/anthonyturner/MCPs/image-analysis-server')
from enhanced_image_analysis_server import EnhancedImageAnalysisServer
import asyncio
from pathlib import Path
import json
async def demo_analysis():
print("š Enhanced Image Analysis MCP Server Demo")
print("=" * 50)
server = EnhancedImageAnalysisServer()
# Demo 1: Analyze a single image with detailed analysis
print("\nšÆ DEMO 1: Single Image Analysis")
print("-" * 30)
test_images = [
"/Users/anthonyturner/Pictures/2squirrels_tree.jpg",
"/Users/anthonyturner/Pictures/Screenshot 2025-08-18 at 7.50.15 PM.png",
"/Users/anthonyturner/Pictures/2027-maserati-mc20.png"
]
for img_path in test_images:
image_path = Path(img_path)
if image_path.exists():
print(f"\nš Analyzing: {image_path.name}")
analysis = server.advanced_heuristic_analysis(image_path)
# Show different naming styles
styles = ["descriptive", "technical", "artistic"]
for style in styles:
new_name = server.generate_name_from_analysis(analysis, style)
print(f" {style.title()}: {new_name}{image_path.suffix.lower()}")
# Show key analysis data
print(f" šØ Color: {analysis.get('color_family', 'unknown')}")
print(f" š Orientation: {analysis.get('orientation', 'unknown')}")
print(f" š¾ Size: {analysis.get('size_category', 'unknown')}")
print(f" š Type hints: {analysis.get('filename_hints', [])}")
if 'dominant_colors' in analysis:
colors = analysis['dominant_colors'][:3]
color_str = ", ".join([f"{c['hex']} ({c['percentage']:.1f}%)" for c in colors])
print(f" š Top colors: {color_str}")
break # Just show one detailed example
# Demo 2: Directory analysis
print(f"\nšÆ DEMO 2: Directory Analysis")
print("-" * 30)
pictures_dir = Path("/Users/anthonyturner/Pictures")
image_files = server.get_image_files(pictures_dir, False)
print(f"š Found {len(image_files)} images in Pictures folder")
print("\nSuggested names for first 10 images:")
analysis_results = []
for i, image_path in enumerate(image_files[:10], 1):
try:
analysis = server.advanced_heuristic_analysis(image_path)
new_name = server.generate_name_from_analysis(analysis, "descriptive")
print(f"[{i:2}] {image_path.name}")
print(f" ā {new_name}{image_path.suffix.lower()}")
analysis_results.append({
'original': image_path.name,
'suggested': f"{new_name}{image_path.suffix.lower()}",
'color': analysis.get('color_family', 'unknown'),
'orientation': analysis.get('orientation', 'unknown'),
'hints': analysis.get('filename_hints', [])
})
except Exception as e:
print(f"[{i:2}] ā Error analyzing {image_path.name}: {e}")
# Demo 3: Organization analysis
print(f"\nšÆ DEMO 3: Content Organization Analysis")
print("-" * 30)
# Categorize images by content
categories = {}
for result in analysis_results:
category = "miscellaneous"
if 'screenshot' in result['hints']:
category = "screenshots"
elif 'photo' in result['hints']:
category = "photos"
elif result['orientation'] == 'portrait':
category = "portraits"
elif result['orientation'] == 'landscape':
category = "landscapes"
elif result['color'] in ['black', 'white', 'gray']:
category = "black_white"
if category not in categories:
categories[category] = []
categories[category].append(result['original'])
print("š Proposed organization:")
for category, files in categories.items():
print(f" š {category.replace('_', ' ').title()} ({len(files)} files)")
for filename in files[:3]: # Show first 3 files
print(f" ā¢ {filename}")
if len(files) > 3:
print(f" ā¢ ... and {len(files) - 3} more")
# Demo 4: Metadata extraction
print(f"\nšÆ DEMO 4: Comprehensive Metadata")
print("-" * 30)
sample_image = Path("/Users/anthonyturner/Pictures/2squirrels_tree.jpg")
if sample_image.exists():
print(f"š Extracting metadata from: {sample_image.name}")
try:
# Basic metadata
with open(sample_image, 'rb') as f:
file_size = len(f.read())
print(f" š File size: {file_size:,} bytes ({file_size/(1024*1024):.2f} MB)")
from PIL import Image
with Image.open(sample_image) as img:
print(f" š Dimensions: {img.width} x {img.height} pixels")
print(f" šØ Color mode: {img.mode}")
print(f" š Format: {img.format}")
# Color analysis
color_analysis = server.analyze_image_colors(sample_image)
if 'dominant_colors' in color_analysis:
print(f" š Dominant color: {color_analysis['color_family']}")
print(f" āļø Brightness: {color_analysis['brightness']:.2f}")
print(f" ā« Grayscale: {color_analysis['is_grayscale']}")
except Exception as e:
print(f" ā Error extracting metadata: {e}")
print(f"\nš Demo completed! The Enhanced Image Analysis MCP Server is ready to use.")
print("=" * 50)
print("\nš Available MCP Tools:")
print(" ā¢ ai_analyze_directory_images - Batch analyze and rename")
print(" ā¢ ai_analyze_single_image - Single image analysis")
print(" ā¢ extract_comprehensive_metadata - Full metadata extraction")
print(" ā¢ organize_images_by_content - Smart folder organization")
print(f"\nāļø To use with Claude Desktop, add this to your config:")
print(' "image-analysis": {')
print(' "command": "python3",')
print(f' "args": ["/Users/anthonyturner/MCPs/image-analysis-server/enhanced_image_analysis_server.py"],')
print(' "env": {}')
print(' }')
if __name__ == "__main__":
asyncio.run(demo_analysis())