mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-11 18:42:49 +00:00
215 lines
8.5 KiB
Python
215 lines
8.5 KiB
Python
import json
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from typing import List, Union, Dict
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import os
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from pathlib import Path
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from loguru import logger
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from tqdm import tqdm
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import asyncio
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from tenacity import retry, stop_after_attempt, RetryError, retry_if_exception_type, wait_exponential
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from google.api_core import exceptions
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import google.generativeai as genai
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import PIL.Image
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import traceback
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from app.utils import utils
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class VisionAnalyzer:
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"""视觉分析器类"""
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def __init__(self, model_name: str = "gemini-1.5-flash", api_key: str = None):
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"""初始化视觉分析器"""
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if not api_key:
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raise ValueError("必须提供API密钥")
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self.model_name = model_name
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self.api_key = api_key
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# 初始化配置
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self._configure_client()
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def _configure_client(self):
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"""配置API客户端"""
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genai.configure(api_key=self.api_key)
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# 开放 Gemini 模型安全设置
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from google.generativeai.types import HarmCategory, HarmBlockThreshold
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safety_settings = {
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HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
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HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
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}
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self.model = genai.GenerativeModel(self.model_name, safety_settings=safety_settings)
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@retry(
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stop=stop_after_attempt(3),
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wait=wait_exponential(multiplier=1, min=4, max=10),
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retry=retry_if_exception_type(exceptions.ResourceExhausted)
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)
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async def _generate_content_with_retry(self, prompt, batch):
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"""使用重试机制的内部方法来调用 generate_content_async"""
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try:
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return await self.model.generate_content_async([prompt, *batch])
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except exceptions.ResourceExhausted as e:
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print(f"API配额限制: {str(e)}")
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raise RetryError("API调用失败")
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async def analyze_images(self,
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images: Union[List[str], List[PIL.Image.Image]],
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prompt: str,
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batch_size: int) -> List[Dict]:
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"""批量分析多张图片"""
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try:
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# 加载图片
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if isinstance(images[0], str):
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logger.info("正在加载图片...")
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images = self.load_images(images)
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# 验证图片列表
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if not images:
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raise ValueError("图片列表为空")
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# 验证每个图片对象
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valid_images = []
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for i, img in enumerate(images):
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if not isinstance(img, PIL.Image.Image):
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logger.error(f"无效的图片对象，索引 {i}: {type(img)}")
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continue
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valid_images.append(img)
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if not valid_images:
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raise ValueError("没有有效的图片对象")
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images = valid_images
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results = []
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total_batches = (len(images) + batch_size - 1) // batch_size
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logger.debug(f"共 {total_batches} 个批次，每批次 {batch_size} 张图片")
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with tqdm(total=total_batches, desc="分析进度") as pbar:
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for i in range(0, len(images), batch_size):
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batch = images[i:i + batch_size]
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retry_count = 0
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while retry_count < 3:
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try:
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# 在每个批次处理前添加小延迟
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if i > 0:
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await asyncio.sleep(2)
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# 确保每个批次的图片都是有效的
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valid_batch = [img for img in batch if isinstance(img, PIL.Image.Image)]
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if not valid_batch:
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raise ValueError(f"批次 {i // batch_size} 中没有有效的图片")
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response = await self._generate_content_with_retry(prompt, valid_batch)
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results.append({
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'batch_index': i // batch_size,
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'images_processed': len(valid_batch),
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'response': response.text,
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'model_used': self.model_name
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})
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break
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except Exception as e:
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retry_count += 1
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error_msg = f"批次 {i // batch_size} 处理出错: {str(e)}"
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logger.error(error_msg)
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if retry_count >= 3:
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results.append({
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'batch_index': i // batch_size,
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'images_processed': len(batch),
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'error': error_msg,
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'model_used': self.model_name
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})
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else:
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logger.info(f"批次 {i // batch_size} 处理失败，等待60秒后重试当前批次...")
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await asyncio.sleep(60)
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pbar.update(1)
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return results
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except Exception as e:
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error_msg = f"图片分析过程中发生错误: {str(e)}\n{traceback.format_exc()}"
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logger.error(error_msg)
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raise Exception(error_msg)
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def save_results_to_txt(self, results: List[Dict], output_dir: str):
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"""将分析结果保存到txt文件"""
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# 确保输出目录存在
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os.makedirs(output_dir, exist_ok=True)
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for result in results:
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if not result.get('image_paths'):
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continue
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response_text = result['response']
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image_paths = result['image_paths']
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# 从文件名中提取时间戳并转换为标准格式
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def format_timestamp(img_path):
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# 从文件名中提取时间部分
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timestamp = Path(img_path).stem.split('_')[-1]
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try:
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# 将时间转换为秒
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seconds = utils.time_to_seconds(timestamp.replace('_', ':'))
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# 转换为 HH:MM:SS,mmm 格式
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hours = int(seconds // 3600)
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minutes = int((seconds % 3600) // 60)
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seconds_remainder = seconds % 60
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whole_seconds = int(seconds_remainder)
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milliseconds = int((seconds_remainder - whole_seconds) * 1000)
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return f"{hours:02d}:{minutes:02d}:{whole_seconds:02d},{milliseconds:03d}"
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except Exception as e:
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logger.error(f"时间戳格式转换错误: {timestamp}, {str(e)}")
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return timestamp
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start_timestamp = format_timestamp(image_paths[0])
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end_timestamp = format_timestamp(image_paths[-1])
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txt_path = os.path.join(output_dir, f"frame_{start_timestamp}_{end_timestamp}.txt")
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# 保存结果到txt文件
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with open(txt_path, 'w', encoding='utf-8') as f:
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f.write(response_text.strip())
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logger.info(f"已保存分析结果到: {txt_path}")
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def load_images(self, image_paths: List[str]) -> List[PIL.Image.Image]:
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"""
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加载多张图片
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Args:
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image_paths: 图片路径列表
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Returns:
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加载后的PIL Image对象列表
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"""
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images = []
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failed_images = []
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for img_path in image_paths:
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try:
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if not os.path.exists(img_path):
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logger.error(f"图片文件不存在: {img_path}")
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failed_images.append(img_path)
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continue
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img = PIL.Image.open(img_path)
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# 确保图片被完全加载
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img.load()
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# 转换为RGB模式
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if img.mode != 'RGB':
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img = img.convert('RGB')
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images.append(img)
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except Exception as e:
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logger.error(f"无法加载图片 {img_path}: {str(e)}")
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failed_images.append(img_path)
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if failed_images:
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logger.warning(f"以下图片加载失败:\n{json.dumps(failed_images, indent=2, ensure_ascii=False)}")
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if not images:
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raise ValueError("没有成功加载任何图片")
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return images |