316 lines
12 KiB
Python
316 lines
12 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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from typing import Optional
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from PIL import Image
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import io
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import math
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import numpy as np
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from ...common.database import db
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from ..config.config import global_config
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from ..models.utils_model import LLM_request
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from src.common.logger import get_module_logger
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logger = get_module_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_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, request_type="image")
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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 db.list_collection_names():
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db.create_collection("images")
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# 删除旧索引
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db.images.drop_indexes()
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# 创建新的复合索引
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db.images.create_index([("hash", 1), ("type", 1)], unique=True)
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db.images.create_index([("url", 1)])
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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 db.list_collection_names():
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db.create_collection("image_descriptions")
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# 删除旧索引
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db.image_descriptions.drop_indexes()
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# 创建新的复合索引
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db.image_descriptions.create_index([("hash", 1), ("type", 1)], unique=True)
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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 = db.image_descriptions.find_one({"hash": image_hash, "type": description_type})
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return result["description"] if result else None
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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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try:
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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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"hash": image_hash, # 确保hash字段存在
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"type": description_type, # 确保type字段存在
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}
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},
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upsert=True,
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)
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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: {str(e)}")
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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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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 查询缓存的描述
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cached_description = self._get_description_from_db(image_hash, "emoji")
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if cached_description:
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logger.debug(f"缓存表情包描述: {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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if image_format == "gif" or image_format == "GIF":
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image_base64 = self.transform_gif(image_base64)
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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，使用中文简洁的描述一下表情包的内容和表达的情感，简短一些"
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64, "jpg")
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else:
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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, image_format)
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cached_description = self._get_description_from_db(image_hash, "emoji")
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if cached_description:
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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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# 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"{timestamp}_{image_hash[:8]}.{image_format}"
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if not os.path.exists(os.path.join(self.IMAGE_DIR, "emoji")):
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os.makedirs(os.path.join(self.IMAGE_DIR, "emoji"))
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file_path = os.path.join(self.IMAGE_DIR, "emoji", 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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db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
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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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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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image_format = Image.open(io.BytesIO(image_bytes)).format.lower()
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# 查询缓存的描述
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cached_description = self._get_description_from_db(image_hash, "image")
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if cached_description:
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logger.debug(f"图片描述缓存中 {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 = (
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"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100个字。"
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)
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description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format)
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cached_description = self._get_description_from_db(image_hash, "image")
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if cached_description:
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logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}")
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return f"[图片：{cached_description}]"
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logger.debug(f"描述是{description}")
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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]"
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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"{timestamp}_{image_hash[:8]}.{image_format}"
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if not os.path.exists(os.path.join(self.IMAGE_DIR, "image")):
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os.makedirs(os.path.join(self.IMAGE_DIR, "image"))
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file_path = os.path.join(self.IMAGE_DIR, "image", 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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db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True)
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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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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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def transform_gif(self, gif_base64: str) -> str:
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"""将GIF转换为水平拼接的静态图像
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Args:
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gif_base64: GIF的base64编码字符串
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Returns:
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str: 拼接后的JPG图像的base64编码字符串
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"""
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try:
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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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frames = []
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try:
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while True:
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gif.seek(len(frames))
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frame = gif.convert('RGB')
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frames.append(frame.copy())
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except EOFError:
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pass
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if not frames:
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raise ValueError("No frames found in GIF")
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# 计算需要抽取的帧的索引
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total_frames = len(frames)
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if total_frames <= 15:
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selected_frames = frames
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else:
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# 均匀抽取10帧
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indices = [int(i * (total_frames - 1) / 14) for i in range(15)]
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selected_frames = [frames[i] for i in indices]
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# 获取单帧的尺寸
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frame_width, frame_height = selected_frames[0].size
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# 计算目标尺寸，保持宽高比
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target_height = 200 # 固定高度
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target_width = int((target_height / frame_height) * frame_width)
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# 调整所有帧的大小
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resized_frames = [frame.resize((target_width, target_height), Image.Resampling.LANCZOS)
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for frame in selected_frames]
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# 创建拼接图像
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total_width = target_width * len(resized_frames)
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combined_image = Image.new('RGB', (total_width, target_height))
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# 水平拼接图像
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for idx, frame in enumerate(resized_frames):
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combined_image.paste(frame, (idx * target_width, 0))
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# 转换为base64
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buffer = io.BytesIO()
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combined_image.save(buffer, format='JPEG', quality=85)
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result_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
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return result_base64
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except Exception as e:
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logger.error(f"GIF转换失败: {str(e)}")
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return None
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# 创建全局单例
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image_manager = ImageManager()
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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
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