import base64 import os import time import hashlib from typing import Optional from PIL import Image import io import numpy as np from ...common.database import db from ...config.config import global_config from ..models.utils_model import LLMRequest from src.common.logger_manager import get_logger logger = get_logger("chat_image") class ImageManager: _instance = None IMAGE_DIR = "data" # 图像存储根目录 def __new__(cls): if cls._instance is None: cls._instance = super().__new__(cls) cls._instance._initialized = False return cls._instance def __init__(self): if not self._initialized: self._ensure_image_collection() self._ensure_description_collection() self._ensure_image_dir() self._initialized = True self._llm = LLMRequest(model=global_config.vlm, temperature=0.4, max_tokens=300, request_type="image") def _ensure_image_dir(self): """确保图像存储目录存在""" os.makedirs(self.IMAGE_DIR, exist_ok=True) @staticmethod def _ensure_image_collection(): """确保images集合存在并创建索引""" if "images" not in db.list_collection_names(): db.create_collection("images") # 删除旧索引 db.images.drop_indexes() # 创建新的复合索引 db.images.create_index([("hash", 1), ("type", 1)], unique=True) db.images.create_index([("url", 1)]) db.images.create_index([("path", 1)]) @staticmethod def _ensure_description_collection(): """确保image_descriptions集合存在并创建索引""" if "image_descriptions" not in db.list_collection_names(): db.create_collection("image_descriptions") # 删除旧索引 db.image_descriptions.drop_indexes() # 创建新的复合索引 db.image_descriptions.create_index([("hash", 1), ("type", 1)], unique=True) @staticmethod def _get_description_from_db(image_hash: str, description_type: str) -> Optional[str]: """从数据库获取图片描述 Args: image_hash: 图片哈希值 description_type: 描述类型 ('emoji' 或 'image') Returns: Optional[str]: 描述文本，如果不存在则返回None """ result = db.image_descriptions.find_one({"hash": image_hash, "type": description_type}) return result["description"] if result else None @staticmethod def _save_description_to_db(image_hash: str, description: str, description_type: str) -> None: """保存图片描述到数据库 Args: image_hash: 图片哈希值 description: 描述文本 description_type: 描述类型 ('emoji' 或 'image') """ try: db.image_descriptions.update_one( {"hash": image_hash, "type": description_type}, { "$set": { "description": description, "timestamp": int(time.time()), "hash": image_hash, # 确保hash字段存在 "type": description_type, # 确保type字段存在 } }, upsert=True, ) except Exception as e: logger.error(f"保存描述到数据库失败: {str(e)}") async def get_emoji_description(self, image_base64: str) -> str: """获取表情包描述，带查重和保存功能""" try: # 计算图片哈希 image_bytes = base64.b64decode(image_base64) image_hash = hashlib.md5(image_bytes).hexdigest() image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # 查询缓存的描述 cached_description = self._get_description_from_db(image_hash, "emoji") if cached_description: # logger.debug(f"缓存表情包描述: {cached_description}") return f"[表达了：{cached_description}]" # 调用AI获取描述 if image_format == "gif" or image_format == "GIF": image_base64 = self.transform_gif(image_base64) prompt = "这是一个动态图表情包，每一张图代表了动态图的某一帧，黑色背景代表透明，使用1-2个词描述一下表情包表达的情感和内容，简短一些" description, _ = await self._llm.generate_response_for_image(prompt, image_base64, "jpg") else: prompt = "这是一个表情包，请用使用几个词描述一下表情包所表达的情感和内容，简短一些" description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format) cached_description = self._get_description_from_db(image_hash, "emoji") if cached_description: logger.warning(f"虽然生成了描述，但是找到缓存表情包描述: {cached_description}") return f"[表达了：{cached_description}]" # 根据配置决定是否保存图片 if global_config.save_emoji: # 生成文件名和路径 timestamp = int(time.time()) filename = f"{timestamp}_{image_hash[:8]}.{image_format}" if not os.path.exists(os.path.join(self.IMAGE_DIR, "emoji")): os.makedirs(os.path.join(self.IMAGE_DIR, "emoji")) file_path = os.path.join(self.IMAGE_DIR, "emoji", filename) try: # 保存文件 with open(file_path, "wb") as f: f.write(image_bytes) # 保存到数据库 image_doc = { "hash": image_hash, "path": file_path, "type": "emoji", "description": description, "timestamp": timestamp, } db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True) logger.trace(f"保存表情包: {file_path}") except Exception as e: logger.error(f"保存表情包文件失败: {str(e)}") # 保存描述到数据库 self._save_description_to_db(image_hash, description, "emoji") return f"[表情包：{description}]" except Exception as e: logger.error(f"获取表情包描述失败: {str(e)}") return "[表情包]" async def get_image_description(self, image_base64: str) -> str: """获取普通图片描述，带查重和保存功能""" try: # 计算图片哈希 image_bytes = base64.b64decode(image_base64) image_hash = hashlib.md5(image_bytes).hexdigest() image_format = Image.open(io.BytesIO(image_bytes)).format.lower() # 查询缓存的描述 cached_description = self._get_description_from_db(image_hash, "image") if cached_description: logger.debug(f"图片描述缓存中 {cached_description}") return f"[图片：{cached_description}]" # 调用AI获取描述 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100个字。" ) description, _ = await self._llm.generate_response_for_image(prompt, image_base64, image_format) cached_description = self._get_description_from_db(image_hash, "image") if cached_description: logger.warning(f"虽然生成了描述，但是找到缓存图片描述 {cached_description}") return f"[图片：{cached_description}]" logger.debug(f"描述是{description}") if description is None: logger.warning("AI未能生成图片描述") return "[图片]" # 根据配置决定是否保存图片 if global_config.save_pic: # 生成文件名和路径 timestamp = int(time.time()) filename = f"{timestamp}_{image_hash[:8]}.{image_format}" if not os.path.exists(os.path.join(self.IMAGE_DIR, "image")): os.makedirs(os.path.join(self.IMAGE_DIR, "image")) file_path = os.path.join(self.IMAGE_DIR, "image", filename) try: # 保存文件 with open(file_path, "wb") as f: f.write(image_bytes) # 保存到数据库 image_doc = { "hash": image_hash, "path": file_path, "type": "image", "description": description, "timestamp": timestamp, } db.images.update_one({"hash": image_hash}, {"$set": image_doc}, upsert=True) logger.trace(f"保存图片: {file_path}") except Exception as e: logger.error(f"保存图片文件失败: {str(e)}") # 保存描述到数据库 self._save_description_to_db(image_hash, description, "image") return f"[图片：{description}]" except Exception as e: logger.error(f"获取图片描述失败: {str(e)}") return "[图片]" @staticmethod def transform_gif(gif_base64: str, similarity_threshold: float = 1000.0, max_frames: int = 15) -> Optional[str]: """将GIF转换为水平拼接的静态图像, 跳过相似的帧 Args: gif_base64: GIF的base64编码字符串 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 max_frames: 最大抽取的帧数，默认15 Returns: 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 """ try: # 解码base64 gif_data = base64.b64decode(gif_base64) gif = Image.open(io.BytesIO(gif_data)) # 收集所有帧 all_frames = [] try: while True: gif.seek(len(all_frames)) # 确保是RGB格式方便比较 frame = gif.convert("RGB") all_frames.append(frame.copy()) except EOFError: pass # 读完啦 if not all_frames: logger.warning("GIF中没有找到任何帧") return None # 空的GIF直接返回None # --- 新的帧选择逻辑 --- selected_frames = [] last_selected_frame_np = None for i, current_frame in enumerate(all_frames): current_frame_np = np.array(current_frame) # 第一帧总是要选的 if i == 0: selected_frames.append(current_frame) last_selected_frame_np = current_frame_np continue # 计算和上一张选中帧的差异（均方误差 MSE） if last_selected_frame_np is not None: mse = np.mean((current_frame_np - last_selected_frame_np) ** 2) # logger.trace(f"帧 {i} 与上一选中帧的 MSE: {mse}") # 可以取消注释来看差异值 # 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 len(resized_frames) > 0: 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 result_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8") return result_base64 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 # 创建全局单例 image_manager = ImageManager() 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: image_data = f.read() if not image_data: raise IOError(f"读取图片文件失败: {image_path}") return base64.b64encode(image_data).decode("utf-8")