Documentation

Overview

MetaX provides comprehensive metadata extraction for images, supporting a wide range of formats including JPEG, PNG, GIF, TIFF, BMP, and WebP. Our extraction engine is built with Python and uses libraries like PIL, ExifRead, and python-magic to deliver detailed analysis of your images.

The metadata extractor can retrieve over 100 different metadata fields, including standard EXIF data, camera settings, GPS information, and even detect potential hidden data in images.

Extraction Capabilities

The following table shows the categories of metadata that can be extracted:

Basic File Information

Detailed information about the image file itself, independent of its content:

Image Properties

Technical details about the image content:

EXIF Data

Our EXIF data extraction is comprehensive, pulling all available EXIF tags from images and organizing them into logical categories.

Key EXIF data fields include:

• Date/Time information (Original, Digitized, Modified)
• Orientation and resolution data
• Color space and bit depth information
• Compression type and format details
• Software used to create or edit the image
• Thumbnail data if available

Camera Information

Detailed information about the camera used to take the photo:

Camera Settings

GPS Data

Geographic location information extracted from the image:

Advanced Features

File Checksums

MetaX generates cryptographic hashes to verify file integrity:

• MD5 hash
• SHA-256 hash

Color Analysis

Advanced color analysis capabilities:

• RGB histogram analysis
• Color distribution statistics
• Palette extraction for indexed color images
• ICC color profile analysis

Embedded Text Extraction

MetaX can identify and extract embedded text data:

• Text strings found in binary data
• XMP metadata detection
• IPTC metadata detection

Thumbnail Handling

Advanced thumbnail extraction and generation:

• EXIF embedded thumbnail extraction
• Dynamic thumbnail generation with proper orientation
• Transparency handling

Hidden Data Detection

MetaX includes technology to detect potential hidden data in images:

AI Analysis Features

MetaX now includes AI-powered analysis capabilities to extract deeper insights from your images:

Visualization Options

MetaX offers multiple visualization options to help you analyze and understand your images:

API Access

MetaX offers an open API for programmatic access to the metadata extraction capabilities. Our API includes rate limiting to ensure fair usage for all users.

Available Endpoints

POST /api/extract

GET /api/status

Request Format

Content-Type: multipart/form-data

Response Format

{
  "filename": "example.jpg",
  "metadata": {
    "basic_info": {...},
    "image_properties": {...},
    "exif_data": {...},
    "checksums": {...},
    "hidden_data": {...},
    "embedded_text": {...}
  },
  "request_info": {
    "timestamp": "2023-08-15T14:22:36.421Z",
    "ip": "198.51.100.42"
  }
}

{
  "status": "operational",
  "version": "1.2.0",
  "rate_limits": {
    "extract_endpoint": "60 per hour; 5 per minute",
    "status_endpoint": "100 per hour"
  },
  "request_info": {
    "timestamp": "2023-08-15T14:23:15.782Z",
    "ip": "198.51.100.42"
  }
}

Error Responses

{
  "error": "Rate limit exceeded",
  "description": "5 per 1 minute",
  "retry_after": 58
}

{
  "error": "No file provided"
}

Code Examples

// Extract metadata from an image
const formData = new FormData();
const fileInput = document.getElementById('file-upload');
formData.append('file', fileInput.files[0]);

fetch('/api/extract', {
    method: 'POST',
    body: formData
})
.then(response => {
    if (!response.ok) {
        throw new Error(`HTTP error! Status: ${response.status}`);
    }
    return response.json();
})
.then(data => {
    console.log('Metadata:', data);
})
.catch(error => {
    console.error('Error:', error);
});

import requests

file_path = "path/to/image.jpg"

# Check API status
response = requests.get("https://metaex.vercel.app/api/status")
print(f"API Status: {response.json()}")

# Upload and extract metadata
with open(file_path, "rb") as image_file:
    files = {"file": (image_file.name, image_file, "image/jpeg")}
    
    response = requests.post(
        "https://metaex.vercel.app/api/extract",
        files=files
    )
    
    if response.status_code == 200:
        metadata = response.json()
        print(f"Metadata successfully extracted from {file_path}")
        print(metadata)
    else:
        print(f"Error: {response.status_code}")
        print(response.json())