mirror of https://github.com/Mai-with-u/MaiBot.git
447 lines
16 KiB
Python
447 lines
16 KiB
Python
import base64
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import io
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import os
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import time
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import zlib
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import aiohttp
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import hashlib
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from typing import Optional, Tuple, Union
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from urllib.parse import urlparse
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from loguru import logger
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from nonebot import get_driver
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from PIL import Image
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from ...common.database import Database
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from ..chat.config import global_config
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from ..models.utils_model import LLM_request
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driver = get_driver()
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config = driver.config
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class ImageManager:
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_instance = None
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IMAGE_DIR = "data" # 图像存储根目录
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def __new__(cls):
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if cls._instance is None:
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cls._instance = super().__new__(cls)
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cls._instance.db = None
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cls._instance._initialized = False
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return cls._instance
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def __init__(self):
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if not self._initialized:
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self.db = Database.get_instance()
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self._ensure_image_collection()
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self._ensure_description_collection()
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self._ensure_image_dir()
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self._initialized = True
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self._llm = LLM_request(model=global_config.vlm, temperature=0.4, max_tokens=300)
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def _ensure_image_dir(self):
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"""确保图像存储目录存在"""
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os.makedirs(self.IMAGE_DIR, exist_ok=True)
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def _ensure_image_collection(self):
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"""确保images集合存在并创建索引"""
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if 'images' not in self.db.db.list_collection_names():
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self.db.db.create_collection('images')
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# 创建索引
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self.db.db.images.create_index([('hash', 1)], unique=True)
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self.db.db.images.create_index([('url', 1)])
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self.db.db.images.create_index([('path', 1)])
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def _ensure_description_collection(self):
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"""确保image_descriptions集合存在并创建索引"""
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if 'image_descriptions' not in self.db.db.list_collection_names():
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self.db.db.create_collection('image_descriptions')
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# 创建索引
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self.db.db.image_descriptions.create_index([('hash', 1)], unique=True)
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self.db.db.image_descriptions.create_index([('type', 1)])
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async def _get_description_from_db(self, image_hash: str, description_type: str) -> Optional[str]:
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"""从数据库获取图片描述
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Args:
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image_hash: 图片哈希值
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description_type: 描述类型 ('emoji' 或 'image')
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Returns:
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Optional[str]: 描述文本，如果不存在则返回None
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"""
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result = await self.db.db.image_descriptions.find_one({
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'hash': image_hash,
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'type': description_type
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})
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return result['description'] if result else None
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async def _save_description_to_db(self, image_hash: str, description: str, description_type: str) -> None:
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"""保存图片描述到数据库
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Args:
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image_hash: 图片哈希值
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description: 描述文本
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description_type: 描述类型 ('emoji' 或 'image')
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"""
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await self.db.db.image_descriptions.update_one(
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{'hash': image_hash, 'type': description_type},
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{
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'$set': {
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'description': description,
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'timestamp': int(time.time())
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}
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},
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upsert=True
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)
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async def save_image(self,
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image_data: Union[str, bytes],
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url: str = None,
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description: str = None,
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is_base64: bool = False) -> Optional[str]:
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"""保存图像
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Args:
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image_data: 图像数据(base64字符串或字节)
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url: 图像URL
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description: 图像描述
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is_base64: image_data是否为base64格式
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Returns:
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str: 保存后的文件路径,失败返回None
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"""
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try:
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# 转换为字节格式
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if is_base64:
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if isinstance(image_data, str):
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image_bytes = base64.b64decode(image_data)
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else:
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return None
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else:
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if isinstance(image_data, bytes):
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image_bytes = image_data
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else:
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return None
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# 计算哈希值
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image_hash = hashlib.md5(image_bytes).hexdigest()
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# 查重
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existing = self.db.db.images.find_one({'hash': image_hash})
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if existing:
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return existing['path']
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR, filename)
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# 保存文件
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with open(file_path, "wb") as f:
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f.write(image_bytes)
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# 保存到数据库
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image_doc = {
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'hash': image_hash,
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'path': file_path,
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'url': url,
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'description': description,
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'timestamp': timestamp
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}
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self.db.db.images.insert_one(image_doc)
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return file_path
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except Exception as e:
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logger.error(f"保存图像失败: {str(e)}")
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return None
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async def get_image_by_url(self, url: str) -> Optional[str]:
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"""根据URL获取图像路径(带查重)
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Args:
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url: 图像URL
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Returns:
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str: 本地文件路径,不存在返回None
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"""
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try:
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# 先查找是否已存在
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existing = self.db.db.images.find_one({'url': url})
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if existing:
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return existing['path']
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# 下载图像
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as resp:
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if resp.status == 200:
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image_bytes = await resp.read()
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return await self.save_image(image_bytes, url=url)
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return None
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except Exception as e:
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logger.error(f"获取图像失败: {str(e)}")
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return None
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async def get_base64_by_url(self, url: str) -> Optional[str]:
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"""根据URL获取base64(带查重)
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Args:
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url: 图像URL
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Returns:
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str: base64字符串,失败返回None
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"""
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try:
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image_path = await self.get_image_by_url(url)
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if not image_path:
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return None
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with open(image_path, 'rb') as f:
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image_bytes = f.read()
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return base64.b64encode(image_bytes).decode('utf-8')
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except Exception as e:
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logger.error(f"获取base64失败: {str(e)}")
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return None
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async def save_base64_image(self, base64_str: str, description: str = None) -> Optional[str]:
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"""保存base64图像(带查重)
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Args:
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base64_str: base64字符串
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description: 图像描述
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Returns:
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str: 保存路径,失败返回None
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"""
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return await self.save_image(base64_str, description=description, is_base64=True)
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def check_url_exists(self, url: str) -> bool:
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"""检查URL是否已存在
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Args:
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url: 图像URL
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Returns:
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bool: 是否存在
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"""
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return self.db.db.images.find_one({'url': url}) is not None
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def check_hash_exists(self, image_data: Union[str, bytes], is_base64: bool = False) -> bool:
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"""检查图像是否已存在
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Args:
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image_data: 图像数据(base64或字节)
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is_base64: 是否为base64格式
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Returns:
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bool: 是否存在
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"""
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try:
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if is_base64:
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if isinstance(image_data, str):
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image_bytes = base64.b64decode(image_data)
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else:
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return False
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else:
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if isinstance(image_data, bytes):
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image_bytes = image_data
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else:
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return False
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image_hash = hashlib.md5(image_bytes).hexdigest()
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return self.db.db.images.find_one({'hash': image_hash}) is not None
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except Exception as e:
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logger.error(f"检查哈希失败: {str(e)}")
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return False
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async def get_emoji_description(self, image_base64: str) -> str:
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"""获取表情包描述，带查重和保存功能"""
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try:
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# 计算图片哈希
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image_bytes = base64.b64decode(image_base64)
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image_hash = hashlib.md5(image_bytes).hexdigest()
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# 查询缓存的描述
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cached_description = await self._get_description_from_db(image_hash, 'emoji')
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if cached_description:
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return f"[表情包：{cached_description}]"
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# 调用AI获取描述
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prompt = "这是一个表情包，使用中文简洁的描述一下表情包的内容和表情包所表达的情感"
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
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# 根据配置决定是否保存图片
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if global_config.EMOJI_SAVE:
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"emoji_{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR, filename)
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try:
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# 保存文件
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with open(file_path, "wb") as f:
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f.write(image_bytes)
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# 保存到数据库
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image_doc = {
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'hash': image_hash,
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'path': file_path,
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'type': 'emoji',
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'description': description,
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'timestamp': timestamp
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}
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await self.db.db.images.update_one(
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{'hash': image_hash},
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{'$set': image_doc},
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upsert=True
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)
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logger.success(f"保存表情包: {file_path}")
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except Exception as e:
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logger.error(f"保存表情包文件失败: {str(e)}")
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# 保存描述到数据库
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await self._save_description_to_db(image_hash, description, 'emoji')
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return f"[表情包：{description}]"
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except Exception as e:
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logger.error(f"获取表情包描述失败: {str(e)}")
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return "[表情包]"
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async def get_image_description(self, image_base64: str) -> str:
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"""获取普通图片描述，带查重和保存功能"""
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try:
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# 计算图片哈希
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image_bytes = base64.b64decode(image_base64)
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image_hash = hashlib.md5(image_bytes).hexdigest()
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# 查询缓存的描述
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cached_description = await self._get_description_from_db(image_hash, 'image')
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if cached_description:
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return f"[图片：{cached_description}]"
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# 调用AI获取描述
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prompt = "请用中文描述这张图片的内容。如果有文字，请把文字都描述出来。并尝试猜测这个图片的含义。最多200个字。"
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64)
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# 根据配置决定是否保存图片
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if global_config.EMOJI_SAVE:
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# 生成文件名和路径
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timestamp = int(time.time())
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filename = f"image_{timestamp}_{image_hash[:8]}.jpg"
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file_path = os.path.join(self.IMAGE_DIR, filename)
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try:
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# 保存文件
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with open(file_path, "wb") as f:
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f.write(image_bytes)
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# 保存到数据库
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image_doc = {
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'hash': image_hash,
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'path': file_path,
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'type': 'image',
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'description': description,
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'timestamp': timestamp
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}
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await self.db.db.images.update_one(
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{'hash': image_hash},
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{'$set': image_doc},
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upsert=True
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)
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logger.success(f"保存图片: {file_path}")
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except Exception as e:
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logger.error(f"保存图片文件失败: {str(e)}")
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# 保存描述到数据库
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await self._save_description_to_db(image_hash, description, 'image')
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return f"[图片：{description}]"
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except Exception as e:
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logger.error(f"获取图片描述失败: {str(e)}")
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return "[图片]"
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# 创建全局单例
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image_manager = ImageManager()
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def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
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"""压缩base64格式的图片到指定大小
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Args:
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base64_data: base64编码的图片数据
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target_size: 目标文件大小（字节），默认0.8MB
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Returns:
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str: 压缩后的base64图片数据
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"""
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try:
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# 将base64转换为字节数据
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image_data = base64.b64decode(base64_data)
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# 如果已经小于目标大小，直接返回原图
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if len(image_data) <= 2*1024*1024:
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return base64_data
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# 将字节数据转换为图片对象
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img = Image.open(io.BytesIO(image_data))
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# 获取原始尺寸
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original_width, original_height = img.size
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# 计算缩放比例
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scale = min(1.0, (target_size / len(image_data)) ** 0.5)
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# 计算新的尺寸
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new_width = int(original_width * scale)
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new_height = int(original_height * scale)
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# 创建内存缓冲区
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output_buffer = io.BytesIO()
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# 如果是GIF，处理所有帧
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if getattr(img, "is_animated", False):
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frames = []
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for frame_idx in range(img.n_frames):
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img.seek(frame_idx)
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new_frame = img.copy()
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new_frame = new_frame.resize((new_width//2, new_height//2), Image.Resampling.LANCZOS) # 动图折上折
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frames.append(new_frame)
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# 保存到缓冲区
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frames[0].save(
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output_buffer,
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format='GIF',
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save_all=True,
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append_images=frames[1:],
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optimize=True,
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duration=img.info.get('duration', 100),
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loop=img.info.get('loop', 0)
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)
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else:
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# 处理静态图片
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resized_img = img.resize((new_width, new_height), Image.Resampling.LANCZOS)
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# 保存到缓冲区，保持原始格式
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if img.format == 'PNG' and img.mode in ('RGBA', 'LA'):
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resized_img.save(output_buffer, format='PNG', optimize=True)
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else:
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resized_img.save(output_buffer, format='JPEG', quality=95, optimize=True)
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# 获取压缩后的数据并转换为base64
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compressed_data = output_buffer.getvalue()
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logger.success(f"压缩图片: {original_width}x{original_height} -> {new_width}x{new_height}")
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logger.info(f"压缩前大小: {len(image_data)/1024:.1f}KB, 压缩后大小: {len(compressed_data)/1024:.1f}KB")
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return base64.b64encode(compressed_data).decode('utf-8')
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except Exception as e:
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logger.error(f"压缩图片失败: {str(e)}")
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import traceback
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logger.error(traceback.format_exc())
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return base64_data
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def image_path_to_base64(image_path: str) -> str:
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"""将图片路径转换为base64编码
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Args:
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image_path: 图片文件路径
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Returns:
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str: base64编码的图片数据
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"""
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try:
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with open(image_path, 'rb') as f:
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image_data = f.read()
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return base64.b64encode(image_data).decode('utf-8')
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except Exception as e:
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logger.error(f"读取图片失败: {image_path}, 错误: {str(e)}")
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return None |