import json from typing import List, Union, Dict import os from pathlib import Path from loguru import logger from tqdm import tqdm import asyncio from tenacity import retry, stop_after_attempt, RetryError, retry_if_exception_type, wait_exponential from google.api_core import exceptions import google.generativeai as genai import PIL.Image import traceback from app.utils import utils class VisionAnalyzer: """视觉分析器类""" def __init__(self, model_name: str = "gemini-1.5-flash", api_key: str = None): """初始化视觉分析器""" if not api_key: raise ValueError("必须提供API密钥") self.model_name = model_name self.api_key = api_key # 初始化配置 self._configure_client() def _configure_client(self): """配置API客户端""" genai.configure(api_key=self.api_key) # 开放 Gemini 模型安全设置 from google.generativeai.types import HarmCategory, HarmBlockThreshold safety_settings = { HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, } self.model = genai.GenerativeModel(self.model_name, safety_settings=safety_settings) @retry( stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10), retry=retry_if_exception_type(exceptions.ResourceExhausted) ) async def _generate_content_with_retry(self, prompt, batch): """使用重试机制的内部方法来调用 generate_content_async""" try: return await self.model.generate_content_async([prompt, *batch]) except exceptions.ResourceExhausted as e: print(f"API配额限制: {str(e)}") raise RetryError("API调用失败") async def analyze_images(self, images: Union[List[str], List[PIL.Image.Image]], prompt: str, batch_size: int) -> List[Dict]: """批量分析多张图片""" try: # 加载图片 if isinstance(images[0], str): images = self.load_images(images) # 验证图片列表 if not images: raise ValueError("图片列表为空") # 验证每个图片对象 valid_images = [] for i, img in enumerate(images): if not isinstance(img, PIL.Image.Image): logger.error(f"无效的图片对象，索引 {i}: {type(img)}") continue valid_images.append(img) if not valid_images: raise ValueError("没有有效的图片对象") images = valid_images results = [] # 视频帧总数除以批量处理大小，如果有小数则+1 batches_needed = len(images) // batch_size if len(images) % batch_size > 0: batches_needed += 1 logger.debug(f"视频帧总数:{len(images)}, 每批处理 {batch_size} 帧, 需要访问 VLM {batches_needed} 次") with tqdm(total=batches_needed, desc="分析进度") as pbar: for i in range(0, len(images), batch_size): batch = images[i:i + batch_size] retry_count = 0 while retry_count < 3: try: # 在每个批次处理前添加小延迟 # if i > 0: # await asyncio.sleep(2) # 确保每个批次的图片都是有效的 valid_batch = [img for img in batch if isinstance(img, PIL.Image.Image)] if not valid_batch: raise ValueError(f"批次 {i // batch_size} 中没有有效的图片") response = await self._generate_content_with_retry(prompt, valid_batch) results.append({ 'batch_index': i // batch_size, 'images_processed': len(valid_batch), 'response': response.text, 'model_used': self.model_name }) break except Exception as e: retry_count += 1 error_msg = f"批次 {i // batch_size} 处理出错: {str(e)}" logger.error(error_msg) if retry_count >= 3: results.append({ 'batch_index': i // batch_size, 'images_processed': len(batch), 'error': error_msg, 'model_used': self.model_name }) else: logger.info(f"批次 {i // batch_size} 处理失败，等待60秒后重试当前批次...") await asyncio.sleep(60) pbar.update(1) return results except Exception as e: error_msg = f"图片分析过程中发生错误: {str(e)}\n{traceback.format_exc()}" logger.error(error_msg) raise Exception(error_msg) def save_results_to_txt(self, results: List[Dict], output_dir: str): """将分析结果保存到txt文件""" # 确保输出目录存在 os.makedirs(output_dir, exist_ok=True) for result in results: if not result.get('image_paths'): continue response_text = result['response'] image_paths = result['image_paths'] # 从文件名中提取时间戳并转换为标准格式 def format_timestamp(img_path): # 从文件名中提取时间部分 timestamp = Path(img_path).stem.split('_')[-1] try: # 将时间转换为秒 seconds = utils.time_to_seconds(timestamp.replace('_', ':')) # 转换为 HH:MM:SS,mmm 格式 hours = int(seconds // 3600) minutes = int((seconds % 3600) // 60) seconds_remainder = seconds % 60 whole_seconds = int(seconds_remainder) milliseconds = int((seconds_remainder - whole_seconds) * 1000) return f"{hours:02d}:{minutes:02d}:{whole_seconds:02d},{milliseconds:03d}" except Exception as e: logger.error(f"时间戳格式转换错误: {timestamp}, {str(e)}") return timestamp start_timestamp = format_timestamp(image_paths[0]) end_timestamp = format_timestamp(image_paths[-1]) txt_path = os.path.join(output_dir, f"frame_{start_timestamp}_{end_timestamp}.txt") # 保存结果到txt文件 with open(txt_path, 'w', encoding='utf-8') as f: f.write(response_text.strip()) logger.info(f"已保存分析结果到: {txt_path}") def load_images(self, image_paths: List[str]) -> List[PIL.Image.Image]: """ 加载多张图片 Args: image_paths: 图片路径列表 Returns: 加载后的PIL Image对象列表 """ images = [] failed_images = [] for img_path in image_paths: try: if not os.path.exists(img_path): logger.error(f"图片文件不存在: {img_path}") failed_images.append(img_path) continue img = PIL.Image.open(img_path) # 确保图片被完全加载 img.load() # 转换为RGB模式 if img.mode != 'RGB': img = img.convert('RGB') images.append(img) except Exception as e: logger.error(f"无法加载图片 {img_path}: {str(e)}") failed_images.append(img_path) if failed_images: logger.warning(f"以下图片加载失败:\n{json.dumps(failed_images, indent=2, ensure_ascii=False)}") if not images: raise ValueError("没有成功加载任何图片") return images