615 lines
26 KiB
Python
615 lines
26 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
视频分析器模块 - 优化版本
|
||
支持多种分析模式：批处理、逐帧、自动选择
|
||
"""
|
||
|
||
import os
|
||
import cv2
|
||
import tempfile
|
||
import asyncio
|
||
import base64
|
||
import hashlib
|
||
import time
|
||
from PIL import Image
|
||
from pathlib import Path
|
||
from typing import List, Tuple, Optional, Dict
|
||
import io
|
||
|
||
from src.llm_models.utils_model import LLMRequest
|
||
from src.config.config import global_config, model_config
|
||
from src.common.logger import get_logger
|
||
from src.common.database.sqlalchemy_models import get_db_session, Videos
|
||
|
||
logger = get_logger("utils_video")
|
||
|
||
# 全局正在处理的视频哈希集合，用于防止重复处理
|
||
processing_videos = set()
|
||
processing_lock = asyncio.Lock()
|
||
# 为每个视频hash创建独立的锁和事件
|
||
video_locks = {}
|
||
video_events = {}
|
||
video_lock_manager = asyncio.Lock()
|
||
|
||
|
||
class VideoAnalyzer:
|
||
"""优化的视频分析器类"""
|
||
|
||
def __init__(self):
|
||
"""初始化视频分析器"""
|
||
# 使用专用的视频分析配置
|
||
try:
|
||
self.video_llm = LLMRequest(
|
||
model_set=model_config.model_task_config.video_analysis,
|
||
request_type="video_analysis"
|
||
)
|
||
logger.info("✅ 使用video_analysis模型配置")
|
||
except (AttributeError, KeyError) as e:
|
||
# 如果video_analysis不存在，使用vlm配置
|
||
self.video_llm = LLMRequest(
|
||
model_set=model_config.model_task_config.vlm,
|
||
request_type="vlm"
|
||
)
|
||
logger.warning(f"video_analysis配置不可用({e})，回退使用vlm配置")
|
||
|
||
# 从配置文件读取参数，如果配置不存在则使用默认值
|
||
try:
|
||
config = global_config.video_analysis
|
||
self.max_frames = config.max_frames
|
||
self.frame_quality = config.frame_quality
|
||
self.max_image_size = config.max_image_size
|
||
self.enable_frame_timing = config.enable_frame_timing
|
||
self.batch_analysis_prompt = config.batch_analysis_prompt
|
||
self.frame_extraction_mode = config.frame_extraction_mode
|
||
self.frame_interval_seconds = config.frame_interval_seconds
|
||
|
||
# 将配置文件中的模式映射到内部使用的模式名称
|
||
config_mode = config.analysis_mode
|
||
if config_mode == "batch_frames":
|
||
self.analysis_mode = "batch"
|
||
elif config_mode == "frame_by_frame":
|
||
self.analysis_mode = "sequential"
|
||
elif config_mode == "auto":
|
||
self.analysis_mode = "auto"
|
||
else:
|
||
logger.warning(f"无效的分析模式: {config_mode}，使用默认的auto模式")
|
||
self.analysis_mode = "auto"
|
||
|
||
self.frame_analysis_delay = 0.3 # API调用间隔（秒）
|
||
self.frame_interval = 1.0 # 抽帧时间间隔（秒）
|
||
self.batch_size = 3 # 批处理时每批处理的帧数
|
||
self.timeout = 60.0 # 分析超时时间（秒）
|
||
logger.info("✅ 从配置文件读取视频分析参数")
|
||
|
||
except AttributeError as e:
|
||
# 如果配置不存在，使用代码中的默认值
|
||
logger.warning(f"配置文件中缺少video_analysis配置({e})，使用默认值")
|
||
self.max_frames = 6
|
||
self.frame_quality = 85
|
||
self.max_image_size = 600
|
||
self.analysis_mode = "auto"
|
||
self.frame_analysis_delay = 0.3
|
||
self.frame_interval = 1.0 # 抽帧时间间隔（秒）
|
||
self.batch_size = 3 # 批处理时每批处理的帧数
|
||
self.timeout = 60.0 # 分析超时时间（秒）
|
||
self.enable_frame_timing = True
|
||
self.frame_extraction_mode = "fixed_number"
|
||
self.frame_interval_seconds = 2.0
|
||
self.batch_analysis_prompt = """请分析这个视频的内容。这些图片是从视频中按时间顺序提取的关键帧。
|
||
|
||
请提供详细的分析，包括：
|
||
1. 视频的整体内容和主题
|
||
2. 主要人物、对象和场景描述
|
||
3. 动作、情节和时间线发展
|
||
4. 视觉风格和艺术特点
|
||
5. 整体氛围和情感表达
|
||
6. 任何特殊的视觉效果或文字内容
|
||
|
||
请用中文回答，分析要详细准确。"""
|
||
|
||
# 系统提示词
|
||
self.system_prompt = "你是一个专业的视频内容分析助手。请仔细观察用户提供的视频关键帧，详细描述视频内容。"
|
||
|
||
logger.info(f"✅ 视频分析器初始化完成，分析模式: {self.analysis_mode}")
|
||
|
||
def _calculate_video_hash(self, video_data: bytes) -> str:
|
||
"""计算视频文件的hash值"""
|
||
hash_obj = hashlib.sha256()
|
||
hash_obj.update(video_data)
|
||
return hash_obj.hexdigest()
|
||
|
||
def _check_video_exists(self, video_hash: str) -> Optional[Videos]:
|
||
"""检查视频是否已经分析过"""
|
||
try:
|
||
with get_db_session() as session:
|
||
# 明确刷新会话以确保看到其他事务的最新提交
|
||
session.expire_all()
|
||
return session.query(Videos).filter(Videos.video_hash == video_hash).first()
|
||
except Exception as e:
|
||
logger.warning(f"检查视频是否存在时出错: {e}")
|
||
return None
|
||
|
||
def _store_video_result(self, video_hash: str, description: str, metadata: Optional[Dict] = None) -> Optional[Videos]:
|
||
"""存储视频分析结果到数据库"""
|
||
try:
|
||
with get_db_session() as session:
|
||
# 只根据video_hash查找
|
||
existing_video = session.query(Videos).filter(
|
||
Videos.video_hash == video_hash
|
||
).first()
|
||
|
||
if existing_video:
|
||
# 如果已存在，更新描述和计数
|
||
existing_video.description = description
|
||
existing_video.count += 1
|
||
existing_video.timestamp = time.time()
|
||
if metadata:
|
||
existing_video.duration = metadata.get('duration')
|
||
existing_video.frame_count = metadata.get('frame_count')
|
||
existing_video.fps = metadata.get('fps')
|
||
existing_video.resolution = metadata.get('resolution')
|
||
existing_video.file_size = metadata.get('file_size')
|
||
session.commit()
|
||
session.refresh(existing_video)
|
||
logger.info(f"✅ 更新已存在的视频记录，hash: {video_hash[:16]}..., count: {existing_video.count}")
|
||
return existing_video
|
||
else:
|
||
video_record = Videos(
|
||
video_hash=video_hash,
|
||
description=description,
|
||
timestamp=time.time(),
|
||
count=1
|
||
)
|
||
if metadata:
|
||
video_record.duration = metadata.get('duration')
|
||
video_record.frame_count = metadata.get('frame_count')
|
||
video_record.fps = metadata.get('fps')
|
||
video_record.resolution = metadata.get('resolution')
|
||
video_record.file_size = metadata.get('file_size')
|
||
|
||
session.add(video_record)
|
||
session.commit()
|
||
session.refresh(video_record)
|
||
logger.info(f"✅ 新视频分析结果已保存到数据库，hash: {video_hash[:16]}...")
|
||
return video_record
|
||
except Exception as e:
|
||
logger.error(f"❌ 存储视频分析结果时出错: {e}")
|
||
return None
|
||
|
||
def set_analysis_mode(self, mode: str):
|
||
"""设置分析模式"""
|
||
if mode in ["batch", "sequential", "auto"]:
|
||
self.analysis_mode = mode
|
||
# logger.info(f"分析模式已设置为: {mode}")
|
||
else:
|
||
logger.warning(f"无效的分析模式: {mode}")
|
||
|
||
async def extract_frames(self, video_path: str) -> List[Tuple[str, float]]:
|
||
"""提取视频帧"""
|
||
frames = []
|
||
cap = cv2.VideoCapture(video_path)
|
||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||
duration = total_frames / fps if fps > 0 else 0
|
||
|
||
logger.info(f"视频信息: {total_frames}帧, {fps:.2f}FPS, {duration:.2f}秒")
|
||
|
||
frame_count = 0
|
||
extracted_count = 0
|
||
|
||
if self.frame_extraction_mode == "time_interval":
|
||
# 新模式：按时间间隔抽帧
|
||
time_interval = self.frame_interval_seconds
|
||
next_frame_time = 0.0
|
||
|
||
while cap.isOpened():
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
|
||
current_time = cap.get(cv2.CAP_PROP_POS_MSEC) / 1000.0
|
||
|
||
if current_time >= next_frame_time:
|
||
# 转换为PIL图像并压缩
|
||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||
pil_image = Image.fromarray(frame_rgb)
|
||
|
||
# 调整图像大小
|
||
if max(pil_image.size) > self.max_image_size:
|
||
ratio = self.max_image_size / max(pil_image.size)
|
||
new_size = tuple(int(dim * ratio) for dim in pil_image.size)
|
||
pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
|
||
|
||
# 转换为base64
|
||
buffer = io.BytesIO()
|
||
pil_image.save(buffer, format='JPEG', quality=self.frame_quality)
|
||
frame_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
|
||
frames.append((frame_base64, current_time))
|
||
extracted_count += 1
|
||
|
||
logger.debug(f"提取第{extracted_count}帧 (时间: {current_time:.2f}s)")
|
||
|
||
next_frame_time += time_interval
|
||
else:
|
||
# 旧模式：固定总帧数
|
||
if duration > 0:
|
||
frame_interval = max(1, int(total_frames / self.max_frames))
|
||
else:
|
||
frame_interval = 1 # 如果无法获取时长，则逐帧提取直到达到max_frames
|
||
|
||
while cap.isOpened() and extracted_count < self.max_frames:
|
||
ret, frame = cap.read()
|
||
if not ret:
|
||
break
|
||
|
||
if frame_count % frame_interval == 0:
|
||
# 转换为PIL图像并压缩
|
||
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
|
||
pil_image = Image.fromarray(frame_rgb)
|
||
|
||
# 调整图像大小
|
||
if max(pil_image.size) > self.max_image_size:
|
||
ratio = self.max_image_size / max(pil_image.size)
|
||
new_size = tuple(int(dim * ratio) for dim in pil_image.size)
|
||
pil_image = pil_image.resize(new_size, Image.Resampling.LANCZOS)
|
||
|
||
# 转换为base64
|
||
buffer = io.BytesIO()
|
||
pil_image.save(buffer, format='JPEG', quality=self.frame_quality)
|
||
frame_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
|
||
|
||
# 计算时间戳
|
||
timestamp = frame_count / fps if fps > 0 else 0
|
||
frames.append((frame_base64, timestamp))
|
||
extracted_count += 1
|
||
|
||
logger.debug(f"提取第{extracted_count}帧 (时间: {timestamp:.2f}s)")
|
||
|
||
frame_count += 1
|
||
|
||
cap.release()
|
||
logger.info(f"✅ 成功提取{len(frames)}帧")
|
||
return frames
|
||
|
||
async def analyze_frames_batch(self, frames: List[Tuple[str, float]], user_question: str = None) -> str:
|
||
"""批量分析所有帧"""
|
||
logger.info(f"开始批量分析{len(frames)}帧")
|
||
|
||
if not frames:
|
||
return "❌ 没有可分析的帧"
|
||
|
||
# 构建提示词
|
||
prompt = self.batch_analysis_prompt
|
||
|
||
if user_question:
|
||
prompt += f"\n\n用户问题: {user_question}"
|
||
|
||
# 添加帧信息到提示词
|
||
frame_info = []
|
||
for i, (_frame_base64, timestamp) in enumerate(frames):
|
||
if self.enable_frame_timing:
|
||
frame_info.append(f"第{i+1}帧 (时间: {timestamp:.2f}s)")
|
||
else:
|
||
frame_info.append(f"第{i+1}帧")
|
||
|
||
prompt += f"\n\n视频包含{len(frames)}帧图像：{', '.join(frame_info)}"
|
||
prompt += "\n\n请基于所有提供的帧图像进行综合分析，关注并描述视频的完整内容和故事发展。"
|
||
|
||
try:
|
||
# 尝试使用多图片分析
|
||
response = await self._analyze_multiple_frames(frames, prompt)
|
||
logger.info("✅ 视频识别完成")
|
||
return response
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 视频识别失败: {e}")
|
||
# 降级到单帧分析
|
||
logger.warning("降级到单帧分析模式")
|
||
try:
|
||
frame_base64, timestamp = frames[0]
|
||
fallback_prompt = prompt + f"\n\n注意：由于技术限制，当前仅显示第1帧 (时间: {timestamp:.2f}s)，视频共有{len(frames)}帧。请基于这一帧进行分析。"
|
||
|
||
response, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=fallback_prompt,
|
||
image_base64=frame_base64,
|
||
image_format="jpeg"
|
||
)
|
||
logger.info("✅ 降级的单帧分析完成")
|
||
return response
|
||
except Exception as fallback_e:
|
||
logger.error(f"❌ 降级分析也失败: {fallback_e}")
|
||
raise
|
||
|
||
async def _analyze_multiple_frames(self, frames: List[Tuple[str, float]], prompt: str) -> str:
|
||
"""使用多图片分析方法"""
|
||
logger.info(f"开始构建包含{len(frames)}帧的分析请求")
|
||
|
||
# 导入MessageBuilder用于构建多图片消息
|
||
from src.llm_models.payload_content.message import MessageBuilder, RoleType
|
||
from src.llm_models.utils_model import RequestType
|
||
|
||
# 构建包含多张图片的消息
|
||
message_builder = MessageBuilder().set_role(RoleType.User).add_text_content(prompt)
|
||
|
||
# 添加所有帧图像
|
||
for _i, (frame_base64, _timestamp) in enumerate(frames):
|
||
message_builder.add_image_content("jpeg", frame_base64)
|
||
# logger.info(f"已添加第{i+1}帧到分析请求 (时间: {timestamp:.2f}s, 图片大小: {len(frame_base64)} chars)")
|
||
|
||
message = message_builder.build()
|
||
# logger.info(f"✅ 多帧消息构建完成，包含{len(frames)}张图片")
|
||
|
||
# 获取模型信息和客户端
|
||
model_info, api_provider, client = self.video_llm._select_model()
|
||
# logger.info(f"使用模型: {model_info.name} 进行多帧分析")
|
||
|
||
# 直接执行多图片请求
|
||
api_response = await self.video_llm._execute_request(
|
||
api_provider=api_provider,
|
||
client=client,
|
||
request_type=RequestType.RESPONSE,
|
||
model_info=model_info,
|
||
message_list=[message],
|
||
temperature=None,
|
||
max_tokens=None
|
||
)
|
||
|
||
logger.info(f"视频识别完成，响应长度: {len(api_response.content or '')} ")
|
||
return api_response.content or "❌ 未获得响应内容"
|
||
|
||
async def analyze_frames_sequential(self, frames: List[Tuple[str, float]], user_question: str = None) -> str:
|
||
"""逐帧分析并汇总"""
|
||
logger.info(f"开始逐帧分析{len(frames)}帧")
|
||
|
||
frame_analyses = []
|
||
|
||
for i, (frame_base64, timestamp) in enumerate(frames):
|
||
try:
|
||
prompt = f"请分析这个视频的第{i+1}帧"
|
||
if self.enable_frame_timing:
|
||
prompt += f" (时间: {timestamp:.2f}s)"
|
||
prompt += "。描述你看到的内容，包括人物、动作、场景、文字等。"
|
||
|
||
if user_question:
|
||
prompt += f"\n特别关注: {user_question}"
|
||
|
||
response, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=prompt,
|
||
image_base64=frame_base64,
|
||
image_format="jpeg"
|
||
)
|
||
|
||
frame_analyses.append(f"第{i+1}帧 ({timestamp:.2f}s): {response}")
|
||
logger.debug(f"✅ 第{i+1}帧分析完成")
|
||
|
||
# API调用间隔
|
||
if i < len(frames) - 1:
|
||
await asyncio.sleep(self.frame_analysis_delay)
|
||
|
||
except Exception as e:
|
||
logger.error(f"❌ 第{i+1}帧分析失败: {e}")
|
||
frame_analyses.append(f"第{i+1}帧: 分析失败 - {e}")
|
||
|
||
# 生成汇总
|
||
logger.info("开始生成汇总分析")
|
||
summary_prompt = f"""基于以下各帧的分析结果，请提供一个完整的视频内容总结：
|
||
|
||
{chr(10).join(frame_analyses)}
|
||
|
||
请综合所有帧的信息，描述视频的整体内容、故事线、主要元素和特点。"""
|
||
|
||
if user_question:
|
||
summary_prompt += f"\n特别回答用户的问题: {user_question}"
|
||
|
||
try:
|
||
# 使用最后一帧进行汇总分析
|
||
if frames:
|
||
last_frame_base64, _ = frames[-1]
|
||
summary, _ = await self.video_llm.generate_response_for_image(
|
||
prompt=summary_prompt,
|
||
image_base64=last_frame_base64,
|
||
image_format="jpeg"
|
||
)
|
||
logger.info("✅ 逐帧分析和汇总完成")
|
||
return summary
|
||
else:
|
||
return "❌ 没有可用于汇总的帧"
|
||
except Exception as e:
|
||
logger.error(f"❌ 汇总分析失败: {e}")
|
||
# 如果汇总失败，返回各帧分析结果
|
||
return f"视频逐帧分析结果：\n\n{chr(10).join(frame_analyses)}"
|
||
|
||
async def analyze_video(self, video_path: str, user_question: str = None) -> str:
|
||
"""分析视频的主要方法"""
|
||
try:
|
||
logger.info(f"开始分析视频: {os.path.basename(video_path)}")
|
||
|
||
# 提取帧
|
||
frames = await self.extract_frames(video_path)
|
||
if not frames:
|
||
return "❌ 无法从视频中提取有效帧"
|
||
|
||
# 根据模式选择分析方法
|
||
if self.analysis_mode == "auto":
|
||
# 智能选择：少于等于3帧用批量，否则用逐帧
|
||
mode = "batch" if len(frames) <= 3 else "sequential"
|
||
logger.info(f"自动选择分析模式: {mode} (基于{len(frames)}帧)")
|
||
else:
|
||
mode = self.analysis_mode
|
||
|
||
# 执行分析
|
||
if mode == "batch":
|
||
result = await self.analyze_frames_batch(frames, user_question)
|
||
else: # sequential
|
||
result = await self.analyze_frames_sequential(frames, user_question)
|
||
|
||
logger.info("✅ 视频分析完成")
|
||
return result
|
||
|
||
except Exception as e:
|
||
error_msg = f"❌ 视频分析失败: {str(e)}"
|
||
logger.error(error_msg)
|
||
return error_msg
|
||
|
||
async def analyze_video_from_bytes(self, video_bytes: bytes, filename: str = None, user_question: str = None, prompt: str = None) -> Dict[str, str]:
|
||
"""从字节数据分析视频
|
||
|
||
Args:
|
||
video_bytes: 视频字节数据
|
||
filename: 文件名（可选，仅用于日志）
|
||
user_question: 用户问题（旧参数名，保持兼容性）
|
||
prompt: 提示词（新参数名，与系统调用保持一致）
|
||
|
||
Returns:
|
||
Dict[str, str]: 包含分析结果的字典，格式为 {"summary": "分析结果"}
|
||
"""
|
||
video_hash = None
|
||
video_event = None
|
||
|
||
try:
|
||
logger.info("开始从字节数据分析视频")
|
||
|
||
# 兼容性处理：如果传入了prompt参数，使用prompt；否则使用user_question
|
||
question = prompt if prompt is not None else user_question
|
||
|
||
# 检查视频数据是否有效
|
||
if not video_bytes:
|
||
return {"summary": "❌ 视频数据为空"}
|
||
|
||
# 计算视频hash值
|
||
video_hash = self._calculate_video_hash(video_bytes)
|
||
logger.info(f"视频hash: {video_hash}")
|
||
|
||
# 改进的并发控制：使用每个视频独立的锁和事件
|
||
async with video_lock_manager:
|
||
if video_hash not in video_locks:
|
||
video_locks[video_hash] = asyncio.Lock()
|
||
video_events[video_hash] = asyncio.Event()
|
||
|
||
video_lock = video_locks[video_hash]
|
||
video_event = video_events[video_hash]
|
||
|
||
# 尝试获取该视频的专用锁
|
||
if video_lock.locked():
|
||
logger.info(f"⏳ 相同视频正在处理中，等待处理完成... (hash: {video_hash[:16]}...)")
|
||
try:
|
||
# 等待处理完成的事件信号，最多等待60秒
|
||
await asyncio.wait_for(video_event.wait(), timeout=60.0)
|
||
logger.info("✅ 等待结束，检查是否有处理结果")
|
||
|
||
# 检查是否有结果了
|
||
existing_video = self._check_video_exists(video_hash)
|
||
if existing_video:
|
||
logger.info(f"✅ 找到了处理结果，直接返回 (id: {existing_video.id})")
|
||
return {"summary": existing_video.description}
|
||
else:
|
||
logger.warning("⚠️ 等待完成但未找到结果，可能处理失败")
|
||
except asyncio.TimeoutError:
|
||
logger.warning("⚠️ 等待超时(60秒)，放弃等待")
|
||
|
||
# 获取锁开始处理
|
||
async with video_lock:
|
||
logger.info(f"🔒 获得视频处理锁，开始处理 (hash: {video_hash[:16]}...)")
|
||
|
||
# 再次检查数据库（可能在等待期间已经有结果了）
|
||
existing_video = self._check_video_exists(video_hash)
|
||
if existing_video:
|
||
logger.info(f"✅ 获得锁后发现已有结果，直接返回 (id: {existing_video.id})")
|
||
video_event.set() # 通知其他等待者
|
||
return {"summary": existing_video.description}
|
||
|
||
# 未找到已存在记录，开始新的分析
|
||
logger.info("未找到已存在的视频记录，开始新的分析")
|
||
|
||
# 创建临时文件进行分析
|
||
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_file:
|
||
temp_file.write(video_bytes)
|
||
temp_path = temp_file.name
|
||
|
||
try:
|
||
# 检查临时文件是否创建成功
|
||
if not os.path.exists(temp_path):
|
||
video_event.set() # 通知等待者
|
||
return {"summary": "❌ 临时文件创建失败"}
|
||
|
||
# 使用临时文件进行分析
|
||
result = await self.analyze_video(temp_path, question)
|
||
|
||
finally:
|
||
# 清理临时文件
|
||
if os.path.exists(temp_path):
|
||
os.unlink(temp_path)
|
||
|
||
# 保存分析结果到数据库
|
||
metadata = {
|
||
"filename": filename,
|
||
"file_size": len(video_bytes),
|
||
"analysis_timestamp": time.time()
|
||
}
|
||
self._store_video_result(
|
||
video_hash=video_hash,
|
||
description=result,
|
||
metadata=metadata
|
||
)
|
||
|
||
# 处理完成，通知等待者并清理资源
|
||
video_event.set()
|
||
async with video_lock_manager:
|
||
# 清理资源
|
||
video_locks.pop(video_hash, None)
|
||
video_events.pop(video_hash, None)
|
||
|
||
return {"summary": result}
|
||
|
||
except Exception as e:
|
||
error_msg = f"❌ 从字节数据分析视频失败: {str(e)}"
|
||
logger.error(error_msg)
|
||
|
||
# 即使失败也保存错误信息到数据库，避免重复处理
|
||
try:
|
||
metadata = {
|
||
"filename": filename,
|
||
"file_size": len(video_bytes),
|
||
"analysis_timestamp": time.time(),
|
||
"error": str(e)
|
||
}
|
||
self._store_video_result(
|
||
video_hash=video_hash,
|
||
description=error_msg,
|
||
metadata=metadata
|
||
)
|
||
logger.info("✅ 错误信息已保存到数据库")
|
||
except Exception as store_e:
|
||
logger.error(f"❌ 保存错误信息失败: {store_e}")
|
||
|
||
# 处理失败，通知等待者并清理资源
|
||
try:
|
||
if video_hash and video_event:
|
||
async with video_lock_manager:
|
||
if video_hash in video_events:
|
||
video_events[video_hash].set()
|
||
video_locks.pop(video_hash, None)
|
||
video_events.pop(video_hash, None)
|
||
except Exception as cleanup_e:
|
||
logger.error(f"❌ 清理锁资源失败: {cleanup_e}")
|
||
|
||
return {"summary": error_msg}
|
||
|
||
def is_supported_video(self, file_path: str) -> bool:
|
||
"""检查是否为支持的视频格式"""
|
||
supported_formats = {'.mp4', '.avi', '.mov', '.mkv', '.flv', '.wmv', '.m4v', '.3gp', '.webm'}
|
||
return Path(file_path).suffix.lower() in supported_formats
|
||
|
||
|
||
# 全局实例
|
||
_video_analyzer = None
|
||
|
||
def get_video_analyzer() -> VideoAnalyzer:
|
||
"""获取视频分析器实例（单例模式）"""
|
||
global _video_analyzer
|
||
if _video_analyzer is None:
|
||
_video_analyzer = VideoAnalyzer()
|
||
return _video_analyzer
|