679 lines
30 KiB
Python
679 lines
30 KiB
Python
import base64
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import os
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import time
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import hashlib
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import uuid
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import io
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import asyncio
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import numpy as np
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from typing import Optional, Tuple
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from PIL import Image
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from rich.traceback import install
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from src.common.logger import get_logger
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from src.common.database.sqlalchemy_models import Images, ImageDescriptions
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from src.config.config import global_config, model_config
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from src.llm_models.utils_model import LLMRequest
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from src.common.database.sqlalchemy_models import get_db_session
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from sqlalchemy import select, and_
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install(extra_lines=3)
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logger = get_logger("chat_image")
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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._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._ensure_image_dir()
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self._initialized = True
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self.vlm = LLMRequest(model_set=model_config.model_task_config.vlm, request_type="image")
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# try:
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# db.connect(reuse_if_open=True)
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# # 使用SQLAlchemy创建表已在初始化时完成
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# logger.debug("使用SQLAlchemy进行表管理")
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# except Exception as e:
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# logger.error(f"数据库连接失败: {e}")
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self._initialized = True
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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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@staticmethod
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def _get_description_from_db(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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try:
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with get_db_session() as session:
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record = session.execute(
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select(ImageDescriptions).where(
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and_(
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ImageDescriptions.image_description_hash == image_hash,
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ImageDescriptions.type == description_type,
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)
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)
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).scalar()
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return record.description if record else None
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except Exception as e:
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logger.error(f"从数据库获取描述失败 (SQLAlchemy): {str(e)}")
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return None
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@staticmethod
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def _save_description_to_db(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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try:
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current_timestamp = time.time()
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with get_db_session() as session:
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# 查找现有记录
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existing = session.execute(
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select(ImageDescriptions).where(
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and_(
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ImageDescriptions.image_description_hash == image_hash,
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ImageDescriptions.type == description_type,
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)
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)
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).scalar()
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if existing:
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# 更新现有记录
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existing.description = description
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existing.timestamp = current_timestamp
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else:
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# 创建新记录
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new_desc = ImageDescriptions(
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image_description_hash=image_hash,
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type=description_type,
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description=description,
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timestamp=current_timestamp,
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)
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session.add(new_desc)
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session.commit()
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# 会在上下文管理器中自动调用
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except Exception as e:
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logger.error(f"保存描述到数据库失败 (SQLAlchemy): {str(e)}")
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async def get_emoji_tag(self, image_base64: str) -> str:
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from src.chat.emoji_system.emoji_manager import get_emoji_manager
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emoji_manager = get_emoji_manager()
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if isinstance(image_base64, str):
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image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
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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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emoji = await emoji_manager.get_emoji_from_manager(image_hash)
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emotion_list = emoji.emotion
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tag_str = ",".join(emotion_list)
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return f"[表情包：{tag_str}]"
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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，优先使用Emoji表中的缓存数据"""
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try:
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# 计算图片哈希
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# 确保base64字符串只包含ASCII字符
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if isinstance(image_base64, str):
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image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
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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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image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
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# 优先使用EmojiManager查询已注册表情包的描述
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try:
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from src.chat.emoji_system.emoji_manager import get_emoji_manager
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emoji_manager = get_emoji_manager()
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cached_emoji_description = await emoji_manager.get_emoji_description_by_hash(image_hash)
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if cached_emoji_description:
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logger.info(f"[缓存命中] 使用已注册表情包描述: {cached_emoji_description[:50]}...")
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return cached_emoji_description
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except Exception as e:
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logger.debug(f"查询EmojiManager时出错: {e}")
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# 查询ImageDescriptions表的缓存描述
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if cached_description := self._get_description_from_db(image_hash, "emoji"):
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logger.info(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
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return f"[表情包：{cached_description}]"
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# === 二步走识别流程 ===
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# 第一步：VLM视觉分析 - 生成详细描述
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if image_format in ["gif", "GIF"]:
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image_base64_processed = self.transform_gif(image_base64)
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if image_base64_processed is None:
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logger.warning("GIF转换失败，无法获取描述")
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return "[表情包(GIF处理失败)]"
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vlm_prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，描述一下表情包表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
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detailed_description, _ = await self.vlm.generate_response_for_image(
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vlm_prompt, image_base64_processed, "jpeg", temperature=0.4, max_tokens=300
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)
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else:
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vlm_prompt = (
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"这是一个表情包，请详细描述一下表情包所表达的情感和内容，描述细节，从互联网梗,meme的角度去分析"
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)
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detailed_description, _ = await self.vlm.generate_response_for_image(
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vlm_prompt, image_base64, image_format, temperature=0.4, max_tokens=300
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)
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if detailed_description is None:
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logger.warning("VLM未能生成表情包详细描述")
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return "[表情包(VLM描述生成失败)]"
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# 第二步：LLM情感分析 - 基于详细描述生成简短的情感标签
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emotion_prompt = f"""
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请你基于这个表情包的详细描述，提取出最核心的情感含义，用1-2个词概括。
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详细描述：'{detailed_description}'
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要求：
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1. 只输出1-2个最核心的情感词汇
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2. 从互联网梗、meme的角度理解
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3. 输出简短精准，不要解释
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4. 如果有多个词用逗号分隔
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"""
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# 使用较低温度确保输出稳定
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emotion_llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="emoji")
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emotion_result, _ = await emotion_llm.generate_response_async(
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emotion_prompt, temperature=0.3, max_tokens=50
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)
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if emotion_result is None:
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logger.warning("LLM未能生成情感标签，使用详细描述的前几个词")
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# 降级处理：从详细描述中提取关键词
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import jieba
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words = list(jieba.cut(detailed_description))
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emotion_result = "，".join(words[:2]) if len(words) >= 2 else (words[0] if words else "表情")
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# 处理情感结果，取前1-2个最重要的标签
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emotions = [e.strip() for e in emotion_result.replace("，", ",").split(",") if e.strip()]
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final_emotion = emotions[0] if emotions else "表情"
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# 如果有第二个情感且不重复，也包含进来
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if len(emotions) > 1 and emotions[1] != emotions[0]:
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final_emotion = f"{emotions[0]}，{emotions[1]}"
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logger.info(f"[emoji识别] 详细描述: {detailed_description[:50]}... -> 情感标签: {final_emotion}")
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if cached_description := self._get_description_from_db(image_hash, "emoji"):
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logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}")
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return f"[表情包：{cached_description}]"
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# 保存表情包文件和元数据（用于可能的后续分析）
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logger.debug(f"保存表情包: {image_hash}")
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current_timestamp = time.time()
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filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
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emoji_dir = os.path.join(self.IMAGE_DIR, "emoji")
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os.makedirs(emoji_dir, exist_ok=True)
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file_path = os.path.join(emoji_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 (Images表) - 包含详细描述用于可能的注册流程
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try:
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from src.common.database.sqlalchemy_models import get_db_session
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with get_db_session() as session:
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existing_img = session.execute(
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select(Images).where(and_(Images.emoji_hash == image_hash, Images.type == "emoji"))
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).scalar()
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if existing_img:
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existing_img.path = file_path
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existing_img.description = detailed_description # 保存详细描述
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existing_img.timestamp = current_timestamp
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else:
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new_img = Images(
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emoji_hash=image_hash,
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path=file_path,
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type="emoji",
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description=detailed_description, # 保存详细描述
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timestamp=current_timestamp,
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)
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session.add(new_img)
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session.commit()
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# 会在上下文管理器中自动调用
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except Exception as e:
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logger.error(f"保存到Images表失败: {str(e)}")
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except Exception as e:
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logger.error(f"保存表情包文件或元数据失败: {str(e)}")
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# 保存最终的情感标签到缓存 (ImageDescriptions表)
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self._save_description_to_db(image_hash, final_emotion, "emoji")
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return f"[表情包：{final_emotion}]"
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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(处理失败)]"
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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，优先使用Images表中的缓存数据"""
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try:
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# 计算图片哈希
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if isinstance(image_base64, str):
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image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
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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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# 优先检查Images表中是否已有完整的描述
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with get_db_session() as session:
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existing_image = session.execute(select(Images).where(Images.emoji_hash == image_hash)).scalar()
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if existing_image:
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# 更新计数
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if hasattr(existing_image, "count") and existing_image.count is not None:
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existing_image.count += 1
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else:
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existing_image.count = 1
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# 如果已有描述，直接返回
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if existing_image.description:
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logger.debug(f"[缓存命中] 使用Images表中的图片描述: {existing_image.description[:50]}...")
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return f"[图片：{existing_image.description}]"
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if cached_description := self._get_description_from_db(image_hash, "image"):
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logger.debug(f"[缓存命中] 使用ImageDescriptions表中的描述: {cached_description[:50]}...")
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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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image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
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prompt = global_config.custom_prompt.image_prompt
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logger.info(f"[VLM调用] 为图片生成新描述 (Hash: {image_hash[:8]}...)")
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description, _ = await self.vlm.generate_response_for_image(
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prompt, image_base64, image_format, temperature=0.4, max_tokens=300
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)
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if description is None:
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logger.warning("AI未能生成图片描述")
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return "[图片(描述生成失败)]"
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# 保存图片和描述
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current_timestamp = time.time()
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filename = f"{int(current_timestamp)}_{image_hash[:8]}.{image_format}"
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image_dir = os.path.join(self.IMAGE_DIR, "image")
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os.makedirs(image_dir, exist_ok=True)
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file_path = os.path.join(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，补充缺失字段
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if existing_image:
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existing_image.path = file_path
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existing_image.description = description
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existing_image.timestamp = current_timestamp
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if not hasattr(existing_image, "image_id") or not existing_image.image_id:
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existing_image.image_id = str(uuid.uuid4())
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if not hasattr(existing_image, "vlm_processed") or existing_image.vlm_processed is None:
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existing_image.vlm_processed = True
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logger.debug(f"[数据库] 更新已有图片记录: {image_hash[:8]}...")
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else:
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new_img = Images(
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image_id=str(uuid.uuid4()),
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emoji_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=current_timestamp,
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vlm_processed=True,
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count=1,
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)
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session.add(new_img)
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logger.debug(f"[数据库] 创建新图片记录: {image_hash[:8]}...")
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except Exception as e:
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logger.error(f"保存图片文件或元数据失败: {str(e)}")
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# 保存描述到ImageDescriptions表作为备用缓存
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self._save_description_to_db(image_hash, description, "image")
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logger.info(f"[VLM完成] 图片描述生成: {description[:50]}...")
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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(处理失败)]"
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@staticmethod
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def transform_gif(gif_base64: str, similarity_threshold: float = 1000.0, max_frames: int = 15) -> Optional[str]:
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# sourcery skip: use-contextlib-suppress
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"""将GIF转换为水平拼接的静态图像, 跳过相似的帧
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Args:
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gif_base64: GIF的base64编码字符串
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similarity_threshold: 判定帧相似的阈值 (MSE)，越小表示要求差异越大才算不同帧，默认1000.0
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max_frames: 最大抽取的帧数，默认15
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Returns:
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Optional[str]: 拼接后的JPG图像的base64编码字符串, 或者在失败时返回None
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"""
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try:
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# 确保base64字符串只包含ASCII字符
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if isinstance(gif_base64, str):
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gif_base64 = gif_base64.encode("ascii", errors="ignore").decode("ascii")
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# 解码base64
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gif_data = base64.b64decode(gif_base64)
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gif = Image.open(io.BytesIO(gif_data))
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# 收集所有帧
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all_frames = []
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try:
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while True:
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gif.seek(len(all_frames))
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# 确保是RGB格式方便比较
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frame = gif.convert("RGB")
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all_frames.append(frame.copy())
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except EOFError:
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... # 读完啦
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if not all_frames:
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logger.warning("GIF中没有找到任何帧")
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return None # 空的GIF直接返回None
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# --- 新的帧选择逻辑 ---
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selected_frames = []
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last_selected_frame_np = None
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for i, current_frame in enumerate(all_frames):
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current_frame_np = np.array(current_frame)
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# 第一帧总是要选的
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if i == 0:
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selected_frames.append(current_frame)
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last_selected_frame_np = current_frame_np
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continue
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# 计算和上一张选中帧的差异（均方误差 MSE）
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if last_selected_frame_np is not None:
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mse = np.mean((current_frame_np - last_selected_frame_np) ** 2)
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# logger.debug(f"帧 {i} 与上一选中帧的 MSE: {mse}") # 可以取消注释来看差异值
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|
||
# 如果差异够大，就选它！
|
||
if mse > similarity_threshold:
|
||
selected_frames.append(current_frame)
|
||
last_selected_frame_np = current_frame_np
|
||
# 检查是不是选够了
|
||
if len(selected_frames) >= max_frames:
|
||
# logger.debug(f"已选够 {max_frames} 帧，停止选择。")
|
||
break
|
||
# 如果差异不大就跳过这一帧啦
|
||
|
||
# --- 帧选择逻辑结束 ---
|
||
|
||
# 如果选择后连一帧都没有（比如GIF只有一帧且后续处理失败？）或者原始GIF就没帧，也返回None
|
||
if not selected_frames:
|
||
logger.warning("处理后没有选中任何帧")
|
||
return None
|
||
|
||
# logger.debug(f"总帧数: {len(all_frames)}, 选中帧数: {len(selected_frames)}")
|
||
|
||
# 获取选中的第一帧的尺寸（假设所有帧尺寸一致）
|
||
frame_width, frame_height = selected_frames[0].size
|
||
|
||
# 计算目标尺寸，保持宽高比
|
||
target_height = 200 # 固定高度
|
||
# 防止除以零
|
||
if frame_height == 0:
|
||
logger.error("帧高度为0，无法计算缩放尺寸")
|
||
return None
|
||
target_width = int((target_height / frame_height) * frame_width)
|
||
# 宽度也不能是0
|
||
if target_width == 0:
|
||
logger.warning(f"计算出的目标宽度为0 (原始尺寸 {frame_width}x{frame_height})，调整为1")
|
||
target_width = 1
|
||
|
||
# 调整所有选中帧的大小
|
||
resized_frames = [
|
||
frame.resize((target_width, target_height), Image.Resampling.LANCZOS) for frame in selected_frames
|
||
]
|
||
|
||
# 创建拼接图像
|
||
total_width = target_width * len(resized_frames)
|
||
# 防止总宽度为0
|
||
if total_width == 0 and resized_frames:
|
||
logger.warning("计算出的总宽度为0，但有选中帧，可能目标宽度太小")
|
||
# 至少给点宽度吧
|
||
total_width = len(resized_frames)
|
||
elif total_width == 0:
|
||
logger.error("计算出的总宽度为0且无选中帧")
|
||
return None
|
||
|
||
combined_image = Image.new("RGB", (total_width, target_height))
|
||
|
||
# 水平拼接图像
|
||
for idx, frame in enumerate(resized_frames):
|
||
combined_image.paste(frame, (idx * target_width, 0))
|
||
|
||
# 转换为base64
|
||
buffer = io.BytesIO()
|
||
combined_image.save(buffer, format="JPEG", quality=85) # 保存为JPEG
|
||
return base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||
except MemoryError:
|
||
logger.error("GIF转换失败: 内存不足，可能是GIF太大或帧数太多")
|
||
return None # 内存不够啦
|
||
except Exception as e:
|
||
logger.error(f"GIF转换失败: {str(e)}", exc_info=True) # 记录详细错误信息
|
||
return None # 其他错误也返回None
|
||
|
||
async def process_image(self, image_base64: str) -> Tuple[str, str]:
|
||
# sourcery skip: hoist-if-from-if
|
||
"""处理图片并返回图片ID和描述
|
||
|
||
Args:
|
||
image_base64: 图片的base64编码
|
||
|
||
Returns:
|
||
Tuple[str, str]: (图片ID, 描述)
|
||
"""
|
||
try:
|
||
# 生成图片ID
|
||
# 计算图片哈希
|
||
# 确保base64字符串只包含ASCII字符
|
||
if isinstance(image_base64, str):
|
||
image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
|
||
image_bytes = base64.b64decode(image_base64)
|
||
image_hash = hashlib.md5(image_bytes).hexdigest()
|
||
with get_db_session() as session:
|
||
existing_image = session.execute(select(Images).where(Images.emoji_hash == image_hash)).scalar()
|
||
if existing_image:
|
||
# 检查是否缺少必要字段，如果缺少则创建新记录
|
||
if (
|
||
not hasattr(existing_image, "image_id")
|
||
or not existing_image.image_id
|
||
or not hasattr(existing_image, "count")
|
||
or existing_image.count is None
|
||
or not hasattr(existing_image, "vlm_processed")
|
||
or existing_image.vlm_processed is None
|
||
):
|
||
logger.debug(f"图片记录缺少必要字段，补全旧记录: {image_hash}")
|
||
if not existing_image.image_id:
|
||
existing_image.image_id = str(uuid.uuid4())
|
||
if existing_image.count is None:
|
||
existing_image.count = 0
|
||
if existing_image.vlm_processed is None:
|
||
existing_image.vlm_processed = False
|
||
|
||
existing_image.count += 1
|
||
|
||
return existing_image.image_id, f"[picid:{existing_image.image_id}]"
|
||
|
||
# print(f"图片不存在: {image_hash}")
|
||
image_id = str(uuid.uuid4())
|
||
|
||
# 保存新图片
|
||
current_timestamp = time.time()
|
||
image_dir = os.path.join(self.IMAGE_DIR, "images")
|
||
os.makedirs(image_dir, exist_ok=True)
|
||
filename = f"{image_id}.png"
|
||
file_path = os.path.join(image_dir, filename)
|
||
|
||
# 保存文件
|
||
with open(file_path, "wb") as f:
|
||
f.write(image_bytes)
|
||
|
||
# 保存到数据库
|
||
new_img = Images(
|
||
image_id=image_id,
|
||
emoji_hash=image_hash,
|
||
path=file_path,
|
||
type="image",
|
||
timestamp=current_timestamp,
|
||
vlm_processed=False,
|
||
count=1,
|
||
)
|
||
session.add(new_img)
|
||
session.commit()
|
||
|
||
# 启动异步VLM处理
|
||
asyncio.create_task(self._process_image_with_vlm(image_id, image_base64))
|
||
|
||
return image_id, f"[picid:{image_id}]"
|
||
|
||
except Exception as e:
|
||
logger.error(f"处理图片失败: {str(e)}")
|
||
return "", "[图片]"
|
||
|
||
async def _process_image_with_vlm(self, image_id: str, image_base64: str) -> None:
|
||
"""使用VLM处理图片并更新数据库
|
||
|
||
Args:
|
||
image_id: 图片ID
|
||
image_base64: 图片的base64编码
|
||
"""
|
||
try:
|
||
# 计算图片哈希
|
||
# 确保base64字符串只包含ASCII字符
|
||
if isinstance(image_base64, str):
|
||
image_base64 = image_base64.encode("ascii", errors="ignore").decode("ascii")
|
||
image_bytes = base64.b64decode(image_base64)
|
||
image_hash = hashlib.md5(image_bytes).hexdigest()
|
||
with get_db_session() as session:
|
||
# 获取当前图片记录
|
||
image = session.execute(select(Images).where(Images.image_id == image_id)).scalar()
|
||
|
||
# 优先检查是否已有其他相同哈希的图片记录包含描述
|
||
existing_with_description = session.execute(
|
||
select(Images).where(
|
||
and_(
|
||
Images.emoji_hash == image_hash,
|
||
Images.description.isnot(None),
|
||
Images.description != "",
|
||
Images.id != image.id,
|
||
)
|
||
)
|
||
).scalar()
|
||
if existing_with_description:
|
||
logger.debug(
|
||
f"[缓存复用] 从其他相同图片记录复用描述: {existing_with_description.description[:50]}..."
|
||
)
|
||
image.description = existing_with_description.description
|
||
image.vlm_processed = True
|
||
|
||
# 同时保存到ImageDescriptions表作为备用缓存
|
||
self._save_description_to_db(image_hash, existing_with_description.description, "image")
|
||
return
|
||
|
||
# 检查ImageDescriptions表的缓存描述
|
||
if cached_description := self._get_description_from_db(image_hash, "image"):
|
||
logger.debug(f"[缓存复用] 从ImageDescriptions表复用描述: {cached_description[:50]}...")
|
||
image.description = cached_description
|
||
image.vlm_processed = True
|
||
|
||
return
|
||
|
||
# 获取图片格式
|
||
image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # type: ignore
|
||
|
||
# 构建prompt
|
||
prompt = global_config.custom_prompt.image_prompt
|
||
|
||
# 获取VLM描述
|
||
logger.info(f"[VLM异步调用] 为图片生成描述 (ID: {image_id}, Hash: {image_hash[:8]}...)")
|
||
description, _ = await self.vlm.generate_response_for_image(
|
||
prompt, image_base64, image_format, temperature=0.4, max_tokens=300
|
||
)
|
||
|
||
if description is None:
|
||
logger.warning("VLM未能生成图片描述")
|
||
description = "无法生成描述"
|
||
|
||
if cached_description := self._get_description_from_db(image_hash, "image"):
|
||
logger.warning(f"虽然生成了描述，但是找到缓存图片描述: {cached_description}")
|
||
description = cached_description
|
||
|
||
# 更新数据库
|
||
image.description = description
|
||
image.vlm_processed = True
|
||
|
||
# 保存描述到ImageDescriptions表作为备用缓存
|
||
self._save_description_to_db(image_hash, description, "image")
|
||
|
||
logger.info(f"[VLM异步完成] 图片描述生成: {description[:50]}...")
|
||
|
||
except Exception as e:
|
||
logger.error(f"VLM处理图片失败: {str(e)}")
|
||
|
||
|
||
# 创建全局单例
|
||
image_manager = None
|
||
|
||
|
||
def get_image_manager() -> ImageManager:
|
||
"""获取全局图片管理器单例"""
|
||
global image_manager
|
||
if image_manager is None:
|
||
image_manager = ImageManager()
|
||
return image_manager
|
||
|
||
|
||
def image_path_to_base64(image_path: str) -> str:
|
||
"""将图片路径转换为base64编码
|
||
Args:
|
||
image_path: 图片文件路径
|
||
Returns:
|
||
str: base64编码的图片数据
|
||
Raises:
|
||
FileNotFoundError: 当图片文件不存在时
|
||
IOError: 当读取图片文件失败时
|
||
"""
|
||
if not os.path.exists(image_path):
|
||
raise FileNotFoundError(f"图片文件不存在: {image_path}")
|
||
|
||
with open(image_path, "rb") as f:
|
||
if image_data := f.read():
|
||
return base64.b64encode(image_data).decode("utf-8")
|
||
else:
|
||
raise IOError(f"读取图片文件失败: {image_path}")
|