390 lines
12 KiB
Python
390 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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image-understanding - Enable AI to understand and analyze images using vision API
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This script allows users to analyze images by calling vision API (like OpenAI GPT-4 Vision).
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It can describe image content, extract text, identify objects, and answer questions about images.
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Usage:
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python image_understanding.py --image path/to/image.jpg
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python image_understanding.py --image https://example.com/image.png --prompt "What objects are in this image?"
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python image_understanding.py --image ./screenshot.png --extract-text --describe
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"""
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import argparse
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import json
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import os
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import sys
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from typing import Optional, Dict, Any, List
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from pathlib import Path
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def validate_image_path(image_path: str) -> str:
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"""
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Validate and normalize image path or URL.
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Args:
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image_path: Path to local image or URL
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Returns:
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Validated image path
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Raises:
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ValueError: If image path is invalid or file doesn't exist
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"""
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if image_path.startswith(('http://', 'https://')):
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# URL validation
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if not image_path.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.webp')):
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raise ValueError(f"Invalid image URL format: {image_path}")
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return image_path
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# Local file path
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path = Path(image_path)
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if not path.exists():
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raise ValueError(f"Image file not found: {image_path}")
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if not path.is_file():
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raise ValueError(f"Path is not a file: {image_path}")
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# Check file extension
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valid_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.webp'}
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if path.suffix.lower() not in valid_extensions:
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raise ValueError(f"Invalid image format: {path.suffix}. Supported formats: {', '.join(valid_extensions)}")
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return str(path.absolute())
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def encode_image(image_path: str) -> str:
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"""
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Encode image to base64 string for API upload.
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Args:
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image_path: Path to image file
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Returns:
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Base64 encoded image string
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Raises:
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Exception: If encoding fails
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"""
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import base64
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try:
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with open(image_path, "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
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return encoded_string
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except Exception as e:
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raise Exception(f"Failed to encode image: {str(e)}")
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def call_vision_api(api_key: str, image_path: str, prompt: str, model: str) -> Dict[str, Any]:
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"""
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Call OpenAI Vision API to analyze image.
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Args:
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api_key: OpenAI API key
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image_path: Path to image file or URL
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prompt: Custom prompt for analysis
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model: Model name to use
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Returns:
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API response as dictionary
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Raises:
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Exception: If API call fails
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"""
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import requests
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# Prepare image content
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if image_path.startswith(('http://', 'https://')):
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image_content = {"type": "image_url", "image_url": {"url": image_path}}
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else:
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base64_image = encode_image(image_path)
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image_content = {
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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# Build messages
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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image_content
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]
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}
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]
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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payload = {
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"model": model,
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"messages": messages,
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"max_tokens": 1000
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}
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try:
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response = requests.post(
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"https://api.openai.com/v1/chat/completions",
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headers=headers,
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json=payload,
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timeout=60
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)
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response.raise_for_status()
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return response.json()
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except requests.exceptions.HTTPError as e:
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error_msg = f"API request failed: {e.response.text}"
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try:
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error_data = e.response.json()
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if 'error' in error_data:
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error_msg = f"API error: {error_data['error'].get('message', str(error_data))}"
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except:
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pass
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raise Exception(error_msg)
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except requests.exceptions.RequestException as e:
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raise Exception(f"Network error: {str(e)}")
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def analyze_image(
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api_key: str,
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image_path: str,
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custom_prompt: Optional[str] = None,
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describe: bool = True,
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extract_text: bool = False,
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identify_objects: bool = False,
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model: str = "gpt-4-vision-preview"
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) -> Dict[str, Any]:
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"""
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Analyze image with specified analysis types.
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Args:
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api_key: OpenAI API key
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image_path: Path to image file or URL
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custom_prompt: Optional custom prompt
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describe: Whether to describe the image
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extract_text: Whether to extract text from image
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identify_objects: Whether to identify objects in image
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model: Model to use for analysis
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Returns:
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Analysis results as dictionary
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"""
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# Build analysis prompt
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analysis_tasks = []
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if describe:
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analysis_tasks.append("Describe the image in detail, including objects, people,场景, colors, and overall composition")
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if extract_text:
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analysis_tasks.append("Extract all visible text from the image (OCR)")
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if identify_objects:
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analysis_tasks.append("Identify and list all recognizable objects, people, and elements in the image")
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if custom_prompt:
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prompt = custom_prompt
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else:
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prompt = f"""Please analyze this image and provide the following information:
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1. {'Describe the image content in detail' if describe else ''}
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2. {'Extract all visible text from the image' if extract_text else ''}
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3. {'List all identifiable objects and elements' if identify_objects else ''}
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Please format your response as a structured analysis with clear sections."""
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# Remove empty tasks
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analysis_tasks = [task for task in analysis_tasks if task]
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if not analysis_tasks and not custom_prompt:
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# Default: full analysis
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prompt = "Provide a comprehensive description of this image, including all visible objects, people,场景, text, and any notable details."
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elif not analysis_tasks and custom_prompt:
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prompt = custom_prompt
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# Call API
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response = call_vision_api(api_key, image_path, prompt, model)
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# Parse response
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content = response.get("choices", [{}])[0].get("message", {}).get("content", "")
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# Extract usage information if available
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usage = response.get("usage", {})
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return {
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"success": True,
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"image_path": image_path,
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"model": model,
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"analysis": {
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"description": content if describe else None,
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"extracted_text": content if extract_text else None,
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"objects": content if identify_objects else None,
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"full_response": content
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},
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"usage": {
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"prompt_tokens": usage.get("prompt_tokens", 0),
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"completion_tokens": usage.get("completion_tokens", 0),
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"total_tokens": usage.get("total_tokens", 0)
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}
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}
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def execute(args: argparse.Namespace) -> Dict[str, Any]:
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"""
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Execute the image understanding analysis.
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Args:
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args: Parsed command-line arguments
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Returns:
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Analysis result as dictionary
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"""
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# Get API key
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api_key = args.api_key or os.environ.get("OPENAI_API_KEY")
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if not api_key:
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raise ValueError("API key not provided. Use --api-key or set OPENAI_API_KEY environment variable")
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# Validate image path
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image_path = validate_image_path(args.image)
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# Determine analysis type
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describe = not (args.extract_text or args.identify_objects or args.custom_prompt)
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extract_text = args.extract_text
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identify_objects = args.identify_objects
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custom_prompt = args.custom_prompt
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# Perform analysis
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result = analyze_image(
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api_key=api_key,
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image_path=image_path,
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custom_prompt=custom_prompt,
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describe=describe,
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extract_text=extract_text,
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identify_objects=identify_objects,
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model=args.model
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)
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return result
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def main():
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"""Main entry point for the image understanding script."""
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parser = argparse.ArgumentParser(
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description="Analyze images using AI vision capabilities (OpenAI GPT-4 Vision)",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Basic image description
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python image_understanding.py --image photo.jpg
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# Extract text from image
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python image_understanding.py --image screenshot.png --extract-text
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# Identify objects in image
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python image_understanding.py --image photo.jpg --identify-objects
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# Custom analysis with specific prompt
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python image_understanding.py --image photo.jpg --prompt "What brand is this product?"
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# Using API key from environment variable
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export OPENAI_API_KEY=your_key
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python image_understanding.py --image photo.jpg
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# Using remote image URL
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python image_understanding.py --image "https://example.com/photo.jpg" --describe
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Environment Variables:
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OPENAI_API_KEY Your OpenAI API key (can be used instead of --api-key)
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"""
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)
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# Required arguments
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parser.add_argument(
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"--image",
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required=True,
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help="Path to local image file or URL of image"
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)
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# Optional arguments
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parser.add_argument(
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"--api-key",
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help="OpenAI API key (can also be set via OPENAI_API_KEY environment variable)"
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)
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parser.add_argument(
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"--model",
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default="gpt-4-vision-preview",
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help="Model to use for vision analysis (default: gpt-4-vision-preview)"
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)
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parser.add_argument(
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"--custom-prompt",
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"-p",
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help="Custom prompt for image analysis"
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)
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# Analysis type flags (mutually exclusive with custom prompt)
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analysis_group = parser.add_mutually_exclusive_group()
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analysis_group.add_argument(
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"--describe",
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action="store_true",
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default=True,
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help="Describe the image content (default behavior)"
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)
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analysis_group.add_argument(
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"--extract-text",
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action="store_true",
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help="Extract text from the image (OCR)"
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)
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analysis_group.add_argument(
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"--identify-objects",
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action="store_true",
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help="Identify and list objects in the image"
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)
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# Output options
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parser.add_argument(
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"--compact",
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action="store_true",
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help="Output compact JSON (without indentation)"
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)
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args = parser.parse_args()
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try:
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result = execute(args)
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# Print result
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indent = None if args.compact else 2
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print(json.dumps(result, ensure_ascii=False, indent=indent))
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sys.exit(0)
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except ValueError as e:
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error_result = {
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"success": False,
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"error": {
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"type": "validation_error",
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"message": str(e)
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}
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}
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print(json.dumps(error_result, ensure_ascii=False, indent=2))
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sys.exit(2)
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except Exception as e:
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error_result = {
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"success": False,
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"error": {
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"type": "execution_error",
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"message": str(e)
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}
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}
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print(json.dumps(error_result, ensure_ascii=False, indent=2))
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sys.exit(1)
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if __name__ == "__main__":
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main() |