feat: 新增 account 和 plan 目录
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""
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Image Understanding using Dashscope (Qwen Vision Models)
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This script enables AI to understand and analyze images using Dashscope's
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vision API models (qwen-vl-plus, qwen-vl-max).
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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 "图片里有什么？"
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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
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from pathlib import Path
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# Dashscope 配置
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DASHSCOPE_API_BASE = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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DEFAULT_MODEL = "qwen-vl-plus"
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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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if not image_path.lower().endswith(('.png', '.jpg', '.jpeg', '.gif', '.webp', '.bmp')):
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raise ValueError(f"Invalid image URL format: {image_path}")
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return image_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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valid_extensions = {'.png', '.jpg', '.jpeg', '.gif', '.webp', '.bmp'}
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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: {', '.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.
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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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"""
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import base64
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with open(image_path, "rb") as f:
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return base64.b64encode(f.read()).decode('utf-8')
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def call_dashscope_api(api_key: str, image_path: str, prompt: str, model: str) -> Dict[str, Any]:
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"""
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Call Dashscope Vision API to analyze image.
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Args:
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api_key: Dashscope 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 (qwen-vl-plus or qwen-vl-max)
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Returns:
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API response as dictionary
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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 = {
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"type": "image_url",
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"image_url": {"url": image_path}
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}
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else:
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base64_image = encode_image(image_path)
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# Detect mime type
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ext = Path(image_path).suffix.lower()
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mime_type = "image/jpeg"
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if ext == '.png':
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mime_type = "image/png"
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elif ext == '.gif':
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mime_type = "image/gif"
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elif ext == '.webp':
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mime_type = "image/webp"
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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:{mime_type};base64,{base64_image}"
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}
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}
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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": 1500,
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"temperature": 0.1
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}
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try:
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response = requests.post(
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f"{DASHSCOPE_API_BASE}/chat/completions",
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headers=headers,
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json=payload,
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timeout=90
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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 = DEFAULT_MODEL
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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: Dashscope 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
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model: Model to use
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Returns:
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Analysis results as dictionary
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"""
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# Build prompt based on analysis type
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if custom_prompt:
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prompt = custom_prompt
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else:
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tasks = []
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if describe:
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tasks.append("详细描述这张图片的内容，包括物体、人物、场景、颜色和整体构成")
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if extract_text:
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tasks.append("提取图片中所有可见的文字(OCR)")
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if identify_objects:
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tasks.append("识别并列出图片中所有可辨认的物体、人物和元素")
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if tasks:
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prompt = f"""请分析这张图片，提供以下信息：
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{'；'.join(tasks)}
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请用清晰的分段格式回答。"""
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else:
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prompt = "请对这张图片进行全面详细的描述，包括所有可见的物体、人物、场景、文字和任何值得注意的细节。"
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# Call API
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response = call_dashscope_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 info
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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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"api_provider": "dashscope",
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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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"""Execute the image understanding analysis."""
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# Get API key - 支持 DASHSCOPE_API_KEY 或 OPENAI_API_KEY
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api_key = args.api_key or os.environ.get("DASHSCOPE_API_KEY") or os.environ.get("OPENAI_API_KEY")
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if not api_key:
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raise ValueError(
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"API key not provided. Use --api-key or set DASHSCOPE_API_KEY environment variable"
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)
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# Validate image
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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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model = args.model or DEFAULT_MODEL
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# Analyze
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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=model
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)
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return result
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def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(
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description="使用 Dashscope（通义千问）视觉模型分析图片",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例:
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# 基本图片描述
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python image_understanding.py --image photo.jpg
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# 提取图片中的文字
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python image_understanding.py --image screenshot.png --extract-text
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# 识别图片中的物体
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python image_understanding.py --image photo.jpg --identify-objects
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# 自定义分析提示词
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python image_understanding.py --image photo.jpg --prompt "这个产品多少钱？"
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# 使用环境变量中的 API key
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export DASHSCOPE_API_KEY=your_key
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python image_understanding.py --image photo.jpg
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# 使用网络图片URL
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python image_understanding.py --image "https://example.com/photo.jpg" --describe
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# 使用更强的模型
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python image_understanding.py --image photo.jpg --model qwen-vl-max
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环境变量:
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DASHSCOPE_API_KEY 你的 Dashscope API key
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OPENAI_API_KEY 也可以使用（兼容性支持）
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"""
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)
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# Required
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parser.add_argument(
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"--image", "-i",
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required=True,
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help="本地图片路径或图片URL"
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)
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# Optional
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parser.add_argument(
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"--api-key",
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help="Dashscope API key (也可通过 DASHSCOPE_API_KEY 环境变量设置)"
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)
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parser.add_argument(
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"--model", "-m",
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default=DEFAULT_MODEL,
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choices=["qwen-vl-plus", "qwen-vl-max"],
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help=f"使用的模型 (默认: {DEFAULT_MODEL})"
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)
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parser.add_argument(
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"--custom-prompt", "-p",
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help="自定义图片分析提示词"
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)
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# Analysis type
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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="描述图片内容 (默认行为)"
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)
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analysis_group.add_argument(
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"--extract-text", "-e",
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action="store_true",
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help="从图片提取文字 (OCR)"
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)
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analysis_group.add_argument(
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"--identify-objects", "-o",
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action="store_true",
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help="识别图片中的物体"
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)
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# Output
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parser.add_argument(
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"--compact",
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action="store_true",
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help="输出紧凑JSON (不缩进)"
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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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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()
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