169 lines
4.2 KiB
Markdown
169 lines
4.2 KiB
Markdown
# Multi-HeyGem 视频处理服务
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Multi-HeyGem 是一个分布式视频处理服务，支持多机器并行处理视频和音频合成任务。该服务提供了 RESTful API 接口，可以方便地集成到其他系统中。
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## 功能特点
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- 支持多机器分布式处理
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- 异步任务处理
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- RESTful API 接口
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- 任务状态追踪
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- 支持回调通知
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- 详细的日志记录
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- 自动负载均衡
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## 系统要求
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配置机器信息：
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编辑 `config/machines.json` 文件，添加您的处理机器信息：
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```json
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{
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"machines": [
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{
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"name": "机器名称",
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"host": "机器IP地址",
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"port": 8000,
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"max_concurrent": 1,
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"enabled": true
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}
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]
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}
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```
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## 启动服务
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```bash
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python api.py
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```
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服务将在 `http://0.0.0.0:8101` 启动。
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## API 接口
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通过 `http://localhost:8101/docs` 可查看 api 接口
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### 创建任务
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创建任务接口需要上传文件，以下是使用示例：
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#### 同步生成示例（直接返回结果）
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```python
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import requests
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import json
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# API 服务地址
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API_URL = "http://localhost:8101"
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# 准备文件
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files = {
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'audio': ('audio.mp3', open('./samples/input/audio.mp3', 'rb')),
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'video': ('video.mp4', open('./samples/input/video.mp4', 'rb'))
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}
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# 准备请求数据
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data = {
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'description': '测试任务'
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}
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# 发送同步生成请求
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response = requests.post(
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f"{API_URL}/tasks/sync",
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files=files,
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data=data
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)
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# 处理响应
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if response.status_code == 200:
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# 直接保存返回的视频流
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output_path = './output/result.mp4'
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with open(output_path, 'wb') as f:
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f.write(response.content)
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print(f"视频生成成功！已保存到: {output_path}")
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else:
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print(f"视频生成失败: {response.text}")
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```
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#### 异步调用示例
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```python
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import aiohttp
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import aiofiles
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import asyncio
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async def create_task():
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API_URL = "http://localhost:8101"
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# 准备文件
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audio_file_path = './samples/input/audio.mp3'
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video_file_path = './samples/input/video.mp4'
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# 创建 FormData
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data = aiohttp.FormData()
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# 添加音频文件
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async with aiofiles.open(audio_file_path, 'rb') as f:
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audio_content = await f.read()
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data.add_field(
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'audio_file',
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audio_content,
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filename='audio.mp3',
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content_type='audio/mpeg'
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)
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# 添加视频文件
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async with aiofiles.open(video_file_path, 'rb') as f:
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video_content = await f.read()
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data.add_field(
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'video_file',
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video_content,
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filename='video.mp4',
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content_type='video/mp4'
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)
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# 添加其他参数
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data.add_field('description', '测试任务')
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data.add_field('callback_url', 'http://your-callback-url.com/webhook') # 可选的回调地址
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# 发送请求
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async with aiohttp.ClientSession() as session:
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async with session.post(f"{API_URL}/tasks", data=data) as response:
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if response.status == 200:
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task = await response.json()
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print(f"任务创建成功！任务ID: {task['task_id']}")
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print(f"任务状态: {task['status']}")
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# 查询任务状态
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task_id = task['task_id']
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async with session.get(f"{API_URL}/tasks/{task_id}") as status_response:
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task_status = await status_response.json()
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print(f"当前任务状态: {task_status['status']}")
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else:
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error_text = await response.text()
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print(f"任务创建失败: {error_text}")
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# 运行异步函数
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asyncio.run(create_task())
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```
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### 任务状态说明
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- `pending`: 任务等待处理
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- `processing`: 任务正在处理中
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- `completed`: 任务处理完成
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- `failed`: 任务处理失败
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### 回调通知
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当任务状态发生变化时，如果设置了 `callback_url`，系统会向该地址发送 POST 请求，请求体包含：
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```json
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{
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"task_id": "任务ID",
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"status": "任务状态",
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"completed_at": "完成时间",
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"error_message": "错误信息（如果有）"
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}
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```
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