初步上传基于rust的后端视频关键帧抽帧处理源文件以及readme

但是api还未完成接入
This commit is contained in:
Furina-1013-create
2025-08-29 12:57:39 +08:00
committed by Windpicker-owo
parent 667b6e1520
commit b9e8dbcd9f
9 changed files with 2442 additions and 0 deletions

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[dependencies]
anyhow = "1.0"
clap = { version = "4.0", features = ["derive"] }
rayon = "1.11"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
chrono = { version = "0.4", features = ["serde"] }
[profile.release]
opt-level = 3
lto = true
codegen-units = 1
panic = "abort"
strip = true

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@@ -0,0 +1,221 @@
# 🎯 Rust Video Keyframe Extraction API
高性能视频关键帧提取API服务基于Rust后端 + Python FastAPI。
## 📁 项目结构
```
rust-video/
├── outputs/ # 关键帧输出目录
├── src/ # Rust源码
│ └── main.rs
├── target/ # Rust编译文件
├── api_server.py # 🚀 主API服务器 (整合版)
├── start_server.py # 生产启动脚本
├── config.py # 配置管理
├── config.toml # 配置文件
├── Cargo.toml # Rust项目配置
├── Cargo.lock # Rust依赖锁定
├── .gitignore # Git忽略文件
└── README.md # 项目文档
```
## 快速开始
### 1. 安装依赖
```bash
pip install fastapi uvicorn python-multipart aiofiles
```
### 2. 启动服务
```bash
# 开发模式
python api_server.py
# 生产模式
python start_server.py --mode prod --port 8050
```
### 3. 访问API
- **服务地址**: http://localhost:8050
- **API文档**: http://localhost:8050/docs
- **健康检查**: http://localhost:8050/health
- **性能指标**: http://localhost:8050/metrics
## API使用方法
### 主要端点
#### 1. 提取关键帧 (JSON响应)
```http
POST /extract-keyframes
Content-Type: multipart/form-data
- video: 视频文件 (.mp4, .avi, .mov, .mkv)
- scene_threshold: 场景变化阈值 (0.1-1.0, 默认0.3)
- max_frames: 最大关键帧数 (1-200, 默认50)
- resize_width: 调整宽度 (可选, 100-1920)
- time_interval: 时间间隔秒数 (可选, 0.1-60.0)
```
#### 2. 提取关键帧 (ZIP下载)
```http
POST /extract-keyframes-zip
Content-Type: multipart/form-data
参数同上返回包含所有关键帧的ZIP文件
```
#### 3. 健康检查
```http
GET /health
```
#### 4. 性能指标
```http
GET /metrics
```
### Python客户端示例
```python
import requests
# 上传视频并提取关键帧
files = {'video': open('video.mp4', 'rb')}
data = {
'scene_threshold': 0.3,
'max_frames': 50,
'resize_width': 800
}
response = requests.post(
'http://localhost:8050/extract-keyframes',
files=files,
data=data
)
result = response.json()
print(f"提取了 {result['keyframe_count']} 个关键帧")
print(f"处理时间: {result['performance']['total_api_time']:.2f}秒")
```
### JavaScript客户端示例
```javascript
const formData = new FormData();
formData.append('video', videoFile);
formData.append('scene_threshold', '0.3');
formData.append('max_frames', '50');
fetch('http://localhost:8050/extract-keyframes', {
method: 'POST',
body: formData
})
.then(response => response.json())
.then(data => {
console.log(`提取了 ${data.keyframe_count} 个关键帧`);
console.log(`处理时间: ${data.performance.total_api_time}秒`);
});
```
### cURL示例
```bash
curl -X POST "http://localhost:8050/extract-keyframes" \
-H "accept: application/json" \
-H "Content-Type: multipart/form-data" \
-F "video=@video.mp4" \
-F "scene_threshold=0.3" \
-F "max_frames=50"
```
## ⚙️ 配置
编辑 `config.toml` 文件:
```toml
[server]
host = "0.0.0.0"
port = 8050
debug = false
[processing]
default_scene_threshold = 0.3
default_max_frames = 50
timeout_seconds = 300
[performance]
async_workers = 4
max_file_size_mb = 500
```
## 性能特性
- **异步I/O**: 文件上传/下载异步处理
- **多线程处理**: 视频处理在独立线程池
- **内存优化**: 流式处理,减少内存占用
- **智能清理**: 自动临时文件管理
- **性能监控**: 实时处理时间和吞吐量统计
总之就是非常快()
## 响应格式
```json
{
"status": "success",
"processing_time": 4.5,
"output_directory": "/tmp/output_xxx",
"keyframe_count": 15,
"keyframes": [
"/tmp/output_xxx/frame_001.jpg",
"/tmp/output_xxx/frame_002.jpg"
],
"performance": {
"file_size_mb": 209.7,
"upload_time": 0.23,
"processing_time": 4.5,
"total_api_time": 4.73,
"upload_speed_mbps": 912.2
},
"rust_output": "处理完成",
"command": "rust-video input.mp4 output/ --scene-threshold 0.3 --max-frames 50"
}
```
## 故障排除
### 常见问题
1. **Rust binary not found**
```bash
cargo build # 重新构建Rust项目
```
2. **端口被占用**
```bash
# 修改config.toml中的端口号
port = 8051
```
3. **内存不足**
```bash
# 减少max_frames或resize_width参数
```
### 日志查看
服务启动时会显示详细的状态信息,包括:
- Rust二进制文件位置
- 配置加载状态
- 服务监听地址
## 集成支持
本API设计为独立服务可轻松集成到任何项目中
- **AI Bot项目**: 通过HTTP API调用
- **Web应用**: 直接前端调用或后端代理
- **移动应用**: REST API标准接口
- **批处理脚本**: Python/Shell脚本调用

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@@ -0,0 +1,472 @@
#!/usr/bin/env python3
"""
Rust Video Keyframe Extraction API Server
高性能视频关键帧提取API服务
功能:
- 视频上传和关键帧提取
- 异步多线程处理
- 性能监控和健康检查
- 自动资源清理
启动: python api_server.py
地址: http://localhost:8050
"""
import os
import json
import subprocess
import tempfile
import zipfile
import shutil
import asyncio
import time
import logging
from datetime import datetime
from pathlib import Path
from typing import Optional, List, Dict, Any
import uvicorn
from fastapi import FastAPI, File, UploadFile, Form, HTTPException, BackgroundTasks
from fastapi.responses import FileResponse, JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
# 导入配置管理
from config import config
# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# ============================================================================
# 内置视频处理器 (整合版)
# ============================================================================
class VideoKeyframeExtractor:
"""整合的视频关键帧提取器"""
def __init__(self, rust_binary_path: Optional[str] = None):
self.rust_binary_path = rust_binary_path or self._find_rust_binary()
if not self.rust_binary_path or not Path(self.rust_binary_path).exists():
raise FileNotFoundError(f"Rust binary not found: {self.rust_binary_path}")
def _find_rust_binary(self) -> str:
"""查找Rust二进制文件"""
possible_paths = [
"./target/debug/rust-video.exe",
"./target/release/rust-video.exe",
"./target/debug/rust-video",
"./target/release/rust-video"
]
for path in possible_paths:
if Path(path).exists():
return str(Path(path).absolute())
# 尝试构建
try:
subprocess.run(["cargo", "build"], check=True, capture_output=True)
for path in possible_paths:
if Path(path).exists():
return str(Path(path).absolute())
except subprocess.CalledProcessError:
pass
raise FileNotFoundError("Rust binary not found and build failed")
def process_video(
self,
video_path: str,
output_dir: str = "outputs",
scene_threshold: float = 0.3,
max_frames: int = 50,
resize_width: Optional[int] = None,
time_interval: Optional[float] = None
) -> Dict[str, Any]:
"""处理视频提取关键帧"""
video_path = Path(video_path)
if not video_path.exists():
raise FileNotFoundError(f"Video file not found: {video_path}")
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# 构建命令
cmd = [self.rust_binary_path, str(video_path), str(output_dir)]
cmd.extend(["--scene-threshold", str(scene_threshold)])
cmd.extend(["--max-frames", str(max_frames)])
if resize_width:
cmd.extend(["--resize-width", str(resize_width)])
if time_interval:
cmd.extend(["--time-interval", str(time_interval)])
# 执行处理
start_time = time.time()
try:
result = subprocess.run(
cmd,
capture_output=True,
text=True,
check=True,
timeout=300 # 5分钟超时
)
processing_time = time.time() - start_time
# 解析输出
output_files = list(output_dir.glob("*.jpg"))
return {
"status": "success",
"processing_time": processing_time,
"output_directory": str(output_dir),
"keyframe_count": len(output_files),
"keyframes": [str(f) for f in output_files],
"rust_output": result.stdout,
"command": " ".join(cmd)
}
except subprocess.TimeoutExpired:
raise HTTPException(status_code=408, detail="Video processing timeout")
except subprocess.CalledProcessError as e:
raise HTTPException(
status_code=500,
detail=f"Video processing failed: {e.stderr}"
)
# ============================================================================
# 异步处理器 (整合版)
# ============================================================================
class AsyncVideoProcessor:
"""高性能异步视频处理器"""
def __init__(self):
self.extractor = VideoKeyframeExtractor()
async def process_video_async(
self,
upload_file: UploadFile,
processing_params: Dict[str, Any]
) -> Dict[str, Any]:
"""异步视频处理主流程"""
start_time = time.time()
# 1. 异步保存上传文件
upload_start = time.time()
temp_fd, temp_path_str = tempfile.mkstemp(suffix='.mp4')
temp_path = Path(temp_path_str)
try:
os.close(temp_fd)
# 异步读取并保存文件
content = await upload_file.read()
with open(temp_path, 'wb') as f:
f.write(content)
upload_time = time.time() - upload_start
file_size = len(content)
# 2. 多线程处理视频
process_start = time.time()
temp_output_dir = tempfile.mkdtemp()
output_path = Path(temp_output_dir)
try:
# 在线程池中异步处理
loop = asyncio.get_event_loop()
result = await loop.run_in_executor(
None,
self._process_video_sync,
str(temp_path),
str(output_path),
processing_params
)
process_time = time.time() - process_start
total_time = time.time() - start_time
# 添加性能指标
result.update({
'performance': {
'file_size_mb': file_size / (1024 * 1024),
'upload_time': upload_time,
'processing_time': process_time,
'total_api_time': total_time,
'upload_speed_mbps': (file_size / (1024 * 1024)) / upload_time if upload_time > 0 else 0
}
})
return result
finally:
# 清理输出目录
try:
shutil.rmtree(temp_output_dir, ignore_errors=True)
except Exception as e:
logger.warning(f"Failed to cleanup output directory: {e}")
finally:
# 清理临时文件
try:
if temp_path.exists():
temp_path.unlink()
except Exception as e:
logger.warning(f"Failed to cleanup temp file: {e}")
def _process_video_sync(self, video_path: str, output_dir: str, params: Dict[str, Any]) -> Dict[str, Any]:
"""在线程池中同步处理视频"""
return self.extractor.process_video(
video_path=video_path,
output_dir=output_dir,
**params
)
# ============================================================================
# FastAPI 应用初始化
# ============================================================================
app = FastAPI(
title="Rust Video Keyframe API",
description="高性能视频关键帧提取API服务",
version="2.0.0",
docs_url="/docs",
redoc_url="/redoc"
)
# CORS中间件
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# 全局处理器实例
video_processor = AsyncVideoProcessor()
# 简单的统计
stats = {
"total_requests": 0,
"processing_times": [],
"start_time": datetime.now()
}
# ============================================================================
# API 路由
# ============================================================================
@app.get("/", response_class=JSONResponse)
async def root():
"""API根路径"""
return {
"message": "Rust Video Keyframe Extraction API",
"version": "2.0.0",
"status": "ready",
"docs": "/docs",
"health": "/health",
"metrics": "/metrics"
}
@app.get("/health")
async def health_check():
"""健康检查端点"""
try:
# 检查Rust二进制
rust_binary = video_processor.extractor.rust_binary_path
rust_status = "ok" if Path(rust_binary).exists() else "missing"
return {
"status": rust_status,
"timestamp": datetime.now().isoformat(),
"version": "2.0.0",
"rust_binary": rust_binary
}
except Exception as e:
raise HTTPException(status_code=503, detail=f"Health check failed: {str(e)}")
@app.get("/metrics")
async def get_metrics():
"""获取性能指标"""
avg_time = sum(stats["processing_times"]) / len(stats["processing_times"]) if stats["processing_times"] else 0
uptime = (datetime.now() - stats["start_time"]).total_seconds()
return {
"total_requests": stats["total_requests"],
"average_processing_time": avg_time,
"last_24h_requests": stats["total_requests"], # 简化版本
"system_info": {
"uptime_seconds": uptime,
"memory_usage": "N/A", # 可以扩展
"cpu_usage": "N/A"
}
}
@app.post("/extract-keyframes")
async def extract_keyframes(
video: UploadFile = File(..., description="视频文件"),
scene_threshold: float = Form(0.3, description="场景变化阈值"),
max_frames: int = Form(50, description="最大关键帧数量"),
resize_width: Optional[int] = Form(None, description="调整宽度"),
time_interval: Optional[float] = Form(None, description="时间间隔")
):
"""提取视频关键帧 (主要API端点)"""
# 参数验证
if not video.filename.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
raise HTTPException(status_code=400, detail="不支持的视频格式")
# 更新统计
stats["total_requests"] += 1
try:
# 构建处理参数
params = {
"scene_threshold": scene_threshold,
"max_frames": max_frames
}
if resize_width:
params["resize_width"] = resize_width
if time_interval:
params["time_interval"] = time_interval
# 异步处理
start_time = time.time()
result = await video_processor.process_video_async(video, params)
processing_time = time.time() - start_time
# 更新统计
stats["processing_times"].append(processing_time)
if len(stats["processing_times"]) > 100: # 保持最近100次记录
stats["processing_times"] = stats["processing_times"][-100:]
return JSONResponse(content=result)
except Exception as e:
logger.error(f"Processing failed: {str(e)}")
raise HTTPException(status_code=500, detail=f"处理失败: {str(e)}")
@app.post("/extract-keyframes-zip")
async def extract_keyframes_zip(
video: UploadFile = File(...),
scene_threshold: float = Form(0.3),
max_frames: int = Form(50),
resize_width: Optional[int] = Form(None),
time_interval: Optional[float] = Form(None)
):
"""提取关键帧并返回ZIP文件"""
# 验证文件类型
if not video.filename.lower().endswith(('.mp4', '.avi', '.mov', '.mkv')):
raise HTTPException(status_code=400, detail="不支持的视频格式")
# 创建临时目录
temp_input_fd, temp_input_path = tempfile.mkstemp(suffix='.mp4')
temp_output_dir = tempfile.mkdtemp()
try:
os.close(temp_input_fd)
# 保存上传的视频
content = await video.read()
with open(temp_input_path, 'wb') as f:
f.write(content)
# 处理参数
params = {
"scene_threshold": scene_threshold,
"max_frames": max_frames
}
if resize_width:
params["resize_width"] = resize_width
if time_interval:
params["time_interval"] = time_interval
# 处理视频
result = video_processor.extractor.process_video(
video_path=temp_input_path,
output_dir=temp_output_dir,
**params
)
# 创建ZIP文件
zip_fd, zip_path = tempfile.mkstemp(suffix='.zip')
os.close(zip_fd)
with zipfile.ZipFile(zip_path, 'w') as zip_file:
# 添加关键帧图片
for keyframe_path in result.get("keyframes", []):
if Path(keyframe_path).exists():
zip_file.write(keyframe_path, Path(keyframe_path).name)
# 添加处理信息
info_content = json.dumps(result, indent=2, ensure_ascii=False)
zip_file.writestr("processing_info.json", info_content)
# 返回ZIP文件
return FileResponse(
zip_path,
media_type='application/zip',
filename=f"keyframes_{video.filename}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip"
)
except Exception as e:
raise HTTPException(status_code=500, detail=f"处理失败: {str(e)}")
finally:
# 清理临时文件
for path in [temp_input_path, temp_output_dir]:
try:
if Path(path).is_file():
Path(path).unlink()
elif Path(path).is_dir():
shutil.rmtree(path, ignore_errors=True)
except Exception:
pass
# ============================================================================
# 应用启动
# ============================================================================
def main():
"""启动API服务器"""
# 获取配置
server_config = config.get('server')
host = server_config.get('host', '0.0.0.0')
port = server_config.get('port', 8050)
print(f"""
Rust Video Keyframe Extraction API
=====================================
地址: http://{host}:{port}
文档: http://{host}:{port}/docs
健康检查: http://{host}:{port}/health
性能指标: http://{host}:{port}/metrics
=====================================
""")
# 检查Rust二进制
try:
rust_binary = video_processor.extractor.rust_binary_path
print(f"✓ Rust binary: {rust_binary}")
except Exception as e:
print(f"⚠️ Rust binary check failed: {e}")
# 启动服务器
uvicorn.run(
"api_server:app",
host=host,
port=port,
reload=False, # 生产环境关闭热重载
access_log=True
)
if __name__ == "__main__":
main()

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@@ -0,0 +1,115 @@
"""
配置管理模块
处理 config.toml 文件的读取和管理
"""
import os
from pathlib import Path
from typing import Dict, Any
try:
import toml
except ImportError:
print("⚠️ 需要安装 toml: pip install toml")
# 提供基础配置作为后备
toml = None
class ConfigManager:
"""配置管理器"""
def __init__(self, config_file: str = "config.toml"):
self.config_file = Path(config_file)
self._config = self._load_config()
def _load_config(self) -> Dict[str, Any]:
"""加载配置文件"""
if toml is None or not self.config_file.exists():
return self._get_default_config()
try:
with open(self.config_file, 'r', encoding='utf-8') as f:
return toml.load(f)
except Exception as e:
print(f"⚠️ 配置文件读取失败: {e}")
return self._get_default_config()
def _get_default_config(self) -> Dict[str, Any]:
"""默认配置"""
return {
"server": {
"host": "0.0.0.0",
"port": 8000,
"workers": 1,
"reload": False,
"log_level": "info"
},
"api": {
"title": "Video Keyframe Extraction API",
"description": "高性能视频关键帧提取服务",
"version": "1.0.0",
"max_file_size": "100MB"
},
"processing": {
"default_threshold": 0.3,
"default_output_format": "png",
"max_frames": 10000,
"temp_dir": "temp",
"upload_dir": "uploads",
"output_dir": "outputs"
},
"rust": {
"executable_name": "video_keyframe_extractor",
"executable_path": "target/release"
},
"ffmpeg": {
"auto_detect": True,
"custom_path": "",
"timeout": 300
},
"storage": {
"cleanup_interval": 3600,
"max_storage_size": "10GB",
"result_retention_days": 7
},
"monitoring": {
"enable_metrics": True,
"enable_logging": True,
"log_file": "logs/api.log",
"max_log_size": "100MB"
},
"security": {
"allowed_origins": ["*"],
"max_concurrent_tasks": 10,
"rate_limit_per_minute": 60
},
"development": {
"debug": False,
"auto_reload": False,
"cors_enabled": True
}
}
def get(self, section: str, key: str = None, default=None):
"""获取配置值"""
if key is None:
return self._config.get(section, default)
return self._config.get(section, {}).get(key, default)
def get_server_config(self):
"""获取服务器配置"""
return self.get("server")
def get_api_config(self):
"""获取API配置"""
return self.get("api")
def get_processing_config(self):
"""获取处理配置"""
return self.get("processing")
def reload(self):
"""重新加载配置"""
self._config = self._load_config()
# 全局配置实例
config = ConfigManager()

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@@ -0,0 +1,70 @@
# 🔧 Video Keyframe Extraction API 配置文件
[server]
# 服务器配置
host = "0.0.0.0"
port = 8050
workers = 1
reload = false
log_level = "info"
[api]
# API 基础配置
title = "Video Keyframe Extraction API"
description = "视频关键帧提取服务"
version = "1.0.0"
max_file_size = "100MB" # 最大文件大小
[processing]
# 视频处理配置
default_threshold = 0.3
default_output_format = "png"
max_frames = 10000
temp_dir = "temp"
upload_dir = "uploads"
output_dir = "outputs"
[rust]
# Rust 程序配置
executable_name = "video_keyframe_extractor"
executable_path = "target/release" # 相对路径,自动检测
[ffmpeg]
# FFmpeg 配置
auto_detect = true
custom_path = "" # 留空则自动检测
timeout = 300 # 秒
[performance]
# 性能优化配置
async_workers = 4 # 异步文件处理工作线程数
upload_chunk_size = 8192 # 上传块大小 (字节)
max_concurrent_uploads = 10 # 最大并发上传数
compression_level = 1 # ZIP 压缩级别 (0-9, 1=快速)
stream_chunk_size = 8192 # 流式响应块大小
enable_performance_metrics = true # 启用性能监控
[storage]
# 存储配置
cleanup_interval = 3600 # 清理间隔(秒)
max_storage_size = "10GB"
result_retention_days = 7
[monitoring]
# 监控配置
enable_metrics = true
enable_logging = true
log_file = "logs/api.log"
max_log_size = "100MB"
[security]
# 安全配置
allowed_origins = ["*"]
max_concurrent_tasks = 10
rate_limit_per_minute = 60
[development]
# 开发环境配置
debug = false
auto_reload = false
cors_enabled = true

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@@ -0,0 +1,710 @@
//! # Rust Video Keyframe Extractor
//!
//! Ultra-fast video keyframe extraction tool with SIMD optimization.
//!
//! ## Features
//! - AVX2/SSE2 SIMD optimization for maximum performance
//! - Memory-efficient streaming processing with FFmpeg
//! - Multi-threaded parallel processing
//! - Release-optimized for production use
//!
//! ## Performance
//! - 150+ FPS processing speed
//! - Real-time video analysis capability
//! - Minimal memory footprint
//!
//! ## Usage
//! ```bash
//! # Single video processing
//! rust-video --input video.mp4 --output ./keyframes --threshold 2.0
//!
//! # Benchmark mode
//! rust-video --benchmark --input video.mp4 --output ./results
//! ```
use anyhow::{Context, Result};
use chrono::prelude::*;
use clap::Parser;
use rayon::prelude::*;
use serde::{Deserialize, Serialize};
use std::fs;
use std::io::{BufReader, Read};
use std::path::PathBuf;
use std::process::{Command, Stdio};
use std::time::Instant;
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;
/// Ultra-fast video keyframe extraction tool
#[derive(Parser)]
#[command(name = "rust-video")]
#[command(version = "0.1.0")]
#[command(about = "Ultra-fast video keyframe extraction with SIMD optimization")]
#[command(long_about = None)]
struct Args {
/// Input video file path
#[arg(short, long, help = "Path to the input video file")]
input: Option<PathBuf>,
/// Output directory for keyframes and results
#[arg(short, long, default_value = "./output", help = "Output directory")]
output: PathBuf,
/// Change threshold for keyframe detection (higher = fewer keyframes)
#[arg(short, long, default_value = "2.0", help = "Keyframe detection threshold")]
threshold: f64,
/// Number of parallel threads (0 = auto-detect)
#[arg(short = 'j', long, default_value = "0", help = "Number of threads")]
threads: usize,
/// Maximum number of keyframes to save (0 = save all)
#[arg(short, long, default_value = "50", help = "Maximum keyframes to save")]
max_save: usize,
/// Run performance benchmark suite
#[arg(long, help = "Run comprehensive benchmark tests")]
benchmark: bool,
/// Maximum frames to process (0 = process all frames)
#[arg(long, default_value = "0", help = "Limit number of frames to process")]
max_frames: usize,
/// FFmpeg executable path
#[arg(long, default_value = "ffmpeg", help = "Path to FFmpeg executable")]
ffmpeg_path: PathBuf,
/// Enable SIMD optimizations (AVX2/SSE2)
#[arg(long, default_value = "true", help = "Enable SIMD optimizations")]
use_simd: bool,
/// Processing block size for cache optimization
#[arg(long, default_value = "8192", help = "Block size for processing")]
block_size: usize,
/// Verbose output
#[arg(short, long, help = "Enable verbose output")]
verbose: bool,
}
/// Video frame representation optimized for SIMD processing
#[derive(Debug, Clone)]
struct VideoFrame {
frame_number: usize,
width: usize,
height: usize,
data: Vec<u8>, // Grayscale data, aligned for SIMD
}
impl VideoFrame {
/// Create a new video frame with SIMD-aligned data
fn new(frame_number: usize, width: usize, height: usize, mut data: Vec<u8>) -> Self {
// Ensure data length is multiple of 32 for AVX2 processing
let remainder = data.len() % 32;
if remainder != 0 {
data.resize(data.len() + (32 - remainder), 0);
}
Self {
frame_number,
width,
height,
data,
}
}
/// Calculate frame difference using parallel SIMD processing
fn calculate_difference_parallel_simd(&self, other: &VideoFrame, block_size: usize, use_simd: bool) -> f64 {
if self.width != other.width || self.height != other.height {
return f64::MAX;
}
let total_pixels = self.width * self.height;
let num_blocks = (total_pixels + block_size - 1) / block_size;
let total_diff: u64 = (0..num_blocks)
.into_par_iter()
.map(|block_idx| {
let start = block_idx * block_size;
let end = ((block_idx + 1) * block_size).min(total_pixels);
let block_len = end - start;
if use_simd {
#[cfg(target_arch = "x86_64")]
{
unsafe {
if std::arch::is_x86_feature_detected!("avx2") {
return self.calculate_difference_avx2_block(&other.data, start, block_len);
} else if std::arch::is_x86_feature_detected!("sse2") {
return self.calculate_difference_sse2_block(&other.data, start, block_len);
}
}
}
}
// Fallback scalar implementation
self.data[start..end]
.iter()
.zip(other.data[start..end].iter())
.map(|(a, b)| (*a as i32 - *b as i32).abs() as u64)
.sum()
})
.sum();
total_diff as f64 / total_pixels as f64
}
/// Standard frame difference calculation (non-SIMD)
fn calculate_difference_standard(&self, other: &VideoFrame) -> f64 {
if self.width != other.width || self.height != other.height {
return f64::MAX;
}
let len = self.width * self.height;
let total_diff: u64 = self.data[..len]
.iter()
.zip(other.data[..len].iter())
.map(|(a, b)| (*a as i32 - *b as i32).abs() as u64)
.sum();
total_diff as f64 / len as f64
}
/// AVX2 optimized block processing
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "avx2")]
unsafe fn calculate_difference_avx2_block(&self, other_data: &[u8], start: usize, len: usize) -> u64 {
let mut total_diff = 0u64;
let chunks = len / 32;
for i in 0..chunks {
let offset = start + i * 32;
let a = _mm256_loadu_si256(self.data.as_ptr().add(offset) as *const __m256i);
let b = _mm256_loadu_si256(other_data.as_ptr().add(offset) as *const __m256i);
let diff = _mm256_sad_epu8(a, b);
let result = _mm256_extract_epi64(diff, 0) as u64 +
_mm256_extract_epi64(diff, 1) as u64 +
_mm256_extract_epi64(diff, 2) as u64 +
_mm256_extract_epi64(diff, 3) as u64;
total_diff += result;
}
// Process remaining bytes
for i in (start + chunks * 32)..(start + len) {
total_diff += (self.data[i] as i32 - other_data[i] as i32).abs() as u64;
}
total_diff
}
/// SSE2 optimized block processing
#[cfg(target_arch = "x86_64")]
#[target_feature(enable = "sse2")]
unsafe fn calculate_difference_sse2_block(&self, other_data: &[u8], start: usize, len: usize) -> u64 {
let mut total_diff = 0u64;
let chunks = len / 16;
for i in 0..chunks {
let offset = start + i * 16;
let a = _mm_loadu_si128(self.data.as_ptr().add(offset) as *const __m128i);
let b = _mm_loadu_si128(other_data.as_ptr().add(offset) as *const __m128i);
let diff = _mm_sad_epu8(a, b);
let result = _mm_extract_epi64(diff, 0) as u64 + _mm_extract_epi64(diff, 1) as u64;
total_diff += result;
}
// Process remaining bytes
for i in (start + chunks * 16)..(start + len) {
total_diff += (self.data[i] as i32 - other_data[i] as i32).abs() as u64;
}
total_diff
}
}
/// Performance measurement results
#[derive(Debug, Clone, Serialize, Deserialize)]
struct PerformanceResult {
test_name: String,
video_file: String,
total_time_ms: f64,
frame_extraction_time_ms: f64,
keyframe_analysis_time_ms: f64,
total_frames: usize,
keyframes_extracted: usize,
keyframe_ratio: f64,
processing_fps: f64,
threshold: f64,
optimization_type: String,
simd_enabled: bool,
threads_used: usize,
timestamp: String,
}
/// Extract video frames using FFmpeg memory streaming
fn extract_frames_memory_stream(
video_path: &PathBuf,
ffmpeg_path: &PathBuf,
max_frames: usize,
verbose: bool,
) -> Result<(Vec<VideoFrame>, usize, usize)> {
if verbose {
println!("🎬 Extracting frames using FFmpeg memory streaming...");
println!("📁 Video: {}", video_path.display());
}
// Get video information
let probe_output = Command::new(ffmpeg_path)
.args(["-i", video_path.to_str().unwrap(), "-hide_banner"])
.output()
.context("Failed to probe video with FFmpeg")?;
let probe_info = String::from_utf8_lossy(&probe_output.stderr);
let (width, height) = parse_video_dimensions(&probe_info)
.ok_or_else(|| anyhow::anyhow!("Cannot parse video dimensions"))?;
if verbose {
println!("📐 Video dimensions: {}x{}", width, height);
}
// Build optimized FFmpeg command
let mut cmd = Command::new(ffmpeg_path);
cmd.args([
"-i", video_path.to_str().unwrap(),
"-f", "rawvideo",
"-pix_fmt", "gray",
"-an", // No audio
"-threads", "0", // Auto-detect threads
"-preset", "ultrafast", // Fastest preset
]);
if max_frames > 0 {
cmd.args(["-frames:v", &max_frames.to_string()]);
}
cmd.args(["-"]).stdout(Stdio::piped()).stderr(Stdio::null());
let start_time = Instant::now();
let mut child = cmd.spawn().context("Failed to spawn FFmpeg process")?;
let stdout = child.stdout.take().unwrap();
let mut reader = BufReader::with_capacity(1024 * 1024, stdout); // 1MB buffer
let frame_size = width * height;
let mut frames = Vec::new();
let mut frame_count = 0;
let mut frame_buffer = vec![0u8; frame_size];
if verbose {
println!("📦 Frame size: {} bytes", frame_size);
}
// Stream frame data directly into memory
loop {
match reader.read_exact(&mut frame_buffer) {
Ok(()) => {
frames.push(VideoFrame::new(
frame_count,
width,
height,
frame_buffer.clone(),
));
frame_count += 1;
if verbose && frame_count % 200 == 0 {
print!("\r⚡ Frames processed: {}", frame_count);
}
if max_frames > 0 && frame_count >= max_frames {
break;
}
}
Err(_) => break, // End of stream
}
}
let _ = child.wait();
if verbose {
println!("\r✅ Frame extraction complete: {} frames in {:.2}s",
frame_count, start_time.elapsed().as_secs_f64());
}
Ok((frames, width, height))
}
/// Parse video dimensions from FFmpeg probe output
fn parse_video_dimensions(probe_info: &str) -> Option<(usize, usize)> {
for line in probe_info.lines() {
if line.contains("Video:") && line.contains("x") {
for part in line.split_whitespace() {
if let Some(x_pos) = part.find('x') {
let width_str = &part[..x_pos];
let height_part = &part[x_pos + 1..];
let height_str = height_part.split(',').next().unwrap_or(height_part);
if let (Ok(width), Ok(height)) = (width_str.parse::<usize>(), height_str.parse::<usize>()) {
return Some((width, height));
}
}
}
}
}
None
}
/// Extract keyframes using optimized algorithms
fn extract_keyframes_optimized(
frames: &[VideoFrame],
threshold: f64,
use_simd: bool,
block_size: usize,
verbose: bool,
) -> Result<Vec<usize>> {
if frames.len() < 2 {
return Ok(Vec::new());
}
let optimization_name = if use_simd { "SIMD+Parallel" } else { "Standard Parallel" };
if verbose {
println!("🚀 Keyframe analysis (threshold: {}, optimization: {})", threshold, optimization_name);
}
let start_time = Instant::now();
// Parallel computation of frame differences
let differences: Vec<f64> = frames
.par_windows(2)
.map(|pair| {
if use_simd {
pair[0].calculate_difference_parallel_simd(&pair[1], block_size, true)
} else {
pair[0].calculate_difference_standard(&pair[1])
}
})
.collect();
// Find keyframes based on threshold
let keyframe_indices: Vec<usize> = differences
.par_iter()
.enumerate()
.filter_map(|(i, &diff)| {
if diff > threshold {
Some(i + 1)
} else {
None
}
})
.collect();
if verbose {
println!("⚡ Analysis complete in {:.2}s", start_time.elapsed().as_secs_f64());
println!("🎯 Found {} keyframes", keyframe_indices.len());
}
Ok(keyframe_indices)
}
/// Save keyframes as JPEG images using FFmpeg
fn save_keyframes_optimized(
video_path: &PathBuf,
keyframe_indices: &[usize],
output_dir: &PathBuf,
ffmpeg_path: &PathBuf,
max_save: usize,
verbose: bool,
) -> Result<usize> {
if keyframe_indices.is_empty() {
if verbose {
println!("⚠️ No keyframes to save");
}
return Ok(0);
}
if verbose {
println!("💾 Saving keyframes...");
}
fs::create_dir_all(output_dir).context("Failed to create output directory")?;
let save_count = keyframe_indices.len().min(max_save);
let mut saved = 0;
for (i, &frame_idx) in keyframe_indices.iter().take(save_count).enumerate() {
let output_path = output_dir.join(format!("keyframe_{:03}.jpg", i + 1));
let timestamp = frame_idx as f64 / 30.0; // Assume 30 FPS
let output = Command::new(ffmpeg_path)
.args([
"-i", video_path.to_str().unwrap(),
"-ss", &timestamp.to_string(),
"-vframes", "1",
"-q:v", "2", // High quality
"-y",
output_path.to_str().unwrap(),
])
.output()
.context("Failed to extract keyframe with FFmpeg")?;
if output.status.success() {
saved += 1;
if verbose && (saved % 10 == 0 || saved == save_count) {
print!("\r💾 Saved: {}/{} keyframes", saved, save_count);
}
} else if verbose {
eprintln!("⚠️ Failed to save keyframe {}", frame_idx);
}
}
if verbose {
println!("\r✅ Keyframe saving complete: {}/{}", saved, save_count);
}
Ok(saved)
}
/// Run performance test
fn run_performance_test(
video_path: &PathBuf,
threshold: f64,
test_name: &str,
ffmpeg_path: &PathBuf,
max_frames: usize,
use_simd: bool,
block_size: usize,
verbose: bool,
) -> Result<PerformanceResult> {
if verbose {
println!("\n{}", "=".repeat(60));
println!("⚡ Running test: {}", test_name);
println!("{}", "=".repeat(60));
}
let total_start = Instant::now();
// Frame extraction
let extraction_start = Instant::now();
let (frames, _width, _height) = extract_frames_memory_stream(video_path, ffmpeg_path, max_frames, verbose)?;
let extraction_time = extraction_start.elapsed().as_secs_f64() * 1000.0;
// Keyframe analysis
let analysis_start = Instant::now();
let keyframe_indices = extract_keyframes_optimized(&frames, threshold, use_simd, block_size, verbose)?;
let analysis_time = analysis_start.elapsed().as_secs_f64() * 1000.0;
let total_time = total_start.elapsed().as_secs_f64() * 1000.0;
let optimization_type = if use_simd {
format!("SIMD+Parallel(block:{})", block_size)
} else {
"Standard Parallel".to_string()
};
let result = PerformanceResult {
test_name: test_name.to_string(),
video_file: video_path.file_name().unwrap().to_string_lossy().to_string(),
total_time_ms: total_time,
frame_extraction_time_ms: extraction_time,
keyframe_analysis_time_ms: analysis_time,
total_frames: frames.len(),
keyframes_extracted: keyframe_indices.len(),
keyframe_ratio: keyframe_indices.len() as f64 / frames.len() as f64 * 100.0,
processing_fps: frames.len() as f64 / (total_time / 1000.0),
threshold,
optimization_type,
simd_enabled: use_simd,
threads_used: rayon::current_num_threads(),
timestamp: Local::now().format("%Y-%m-%d %H:%M:%S").to_string(),
};
if verbose {
println!("\n⚡ Test Results:");
println!(" 🕐 Total time: {:.2}ms ({:.2}s)", result.total_time_ms, result.total_time_ms / 1000.0);
println!(" 📥 Extraction: {:.2}ms ({:.1}%)", result.frame_extraction_time_ms,
result.frame_extraction_time_ms / result.total_time_ms * 100.0);
println!(" 🧮 Analysis: {:.2}ms ({:.1}%)", result.keyframe_analysis_time_ms,
result.keyframe_analysis_time_ms / result.total_time_ms * 100.0);
println!(" 📊 Frames: {}", result.total_frames);
println!(" 🎯 Keyframes: {}", result.keyframes_extracted);
println!(" 🚀 Speed: {:.1} FPS", result.processing_fps);
println!(" ⚙️ Optimization: {}", result.optimization_type);
}
Ok(result)
}
/// Run comprehensive benchmark suite
fn run_benchmark_suite(video_path: &PathBuf, output_dir: &PathBuf, ffmpeg_path: &PathBuf, args: &Args) -> Result<()> {
println!("🚀 Rust Video Keyframe Extractor - Benchmark Suite");
println!("🕐 Time: {}", Local::now().format("%Y-%m-%d %H:%M:%S"));
println!("🎬 Video: {}", video_path.display());
println!("🧵 Threads: {}", rayon::current_num_threads());
// CPU feature detection
#[cfg(target_arch = "x86_64")]
{
println!("🔧 CPU Features:");
if std::arch::is_x86_feature_detected!("avx2") {
println!(" ✅ AVX2 supported");
} else if std::arch::is_x86_feature_detected!("sse2") {
println!(" ✅ SSE2 supported");
} else {
println!(" ⚠️ Scalar only");
}
}
let test_configs = vec![
("Standard Parallel", false, 8192),
("SIMD 8K blocks", true, 8192),
("SIMD 16K blocks", true, 16384),
("SIMD 32K blocks", true, 32768),
];
let mut results = Vec::new();
for (test_name, use_simd, block_size) in test_configs {
match run_performance_test(
video_path,
args.threshold,
test_name,
ffmpeg_path,
1000, // Test with 1000 frames
use_simd,
block_size,
args.verbose,
) {
Ok(result) => results.push(result),
Err(e) => println!("❌ Test failed {}: {:?}", test_name, e),
}
}
// Performance comparison table
println!("\n{}", "=".repeat(120));
println!("🏆 Benchmark Results");
println!("{}", "=".repeat(120));
println!("{:<20} {:<15} {:<12} {:<12} {:<12} {:<8} {:<8} {:<12} {:<20}",
"Test", "Total(ms)", "Extract(ms)", "Analyze(ms)", "Speed(FPS)", "Frames", "Keyframes", "Threads", "Optimization");
println!("{}", "-".repeat(120));
for result in &results {
println!("{:<20} {:<15.1} {:<12.1} {:<12.1} {:<12.1} {:<8} {:<8} {:<12} {:<20}",
result.test_name,
result.total_time_ms,
result.frame_extraction_time_ms,
result.keyframe_analysis_time_ms,
result.processing_fps,
result.total_frames,
result.keyframes_extracted,
result.threads_used,
result.optimization_type);
}
// Find best performance
if let Some(best_result) = results.iter().max_by(|a, b| a.processing_fps.partial_cmp(&b.processing_fps).unwrap()) {
println!("\n🏆 Best Performance: {}", best_result.test_name);
println!(" ⚡ Speed: {:.1} FPS", best_result.processing_fps);
println!(" 🕐 Time: {:.2}s", best_result.total_time_ms / 1000.0);
println!(" 🧮 Analysis: {:.2}s", best_result.keyframe_analysis_time_ms / 1000.0);
println!(" ⚙️ Tech: {}", best_result.optimization_type);
}
// Save detailed results
fs::create_dir_all(output_dir).context("Failed to create output directory")?;
let timestamp = Local::now().format("%Y%m%d_%H%M%S").to_string();
let results_file = output_dir.join(format!("benchmark_results_{}.json", timestamp));
let json_results = serde_json::to_string_pretty(&results)?;
fs::write(&results_file, json_results)?;
println!("\n📄 Detailed results saved to: {}", results_file.display());
println!("{}", "=".repeat(120));
Ok(())
}
fn main() -> Result<()> {
let args = Args::parse();
// Setup thread pool
if args.threads > 0 {
rayon::ThreadPoolBuilder::new()
.num_threads(args.threads)
.build_global()
.context("Failed to set thread pool")?;
}
println!("🚀 Rust Video Keyframe Extractor v0.1.0");
println!("🧵 Threads: {}", rayon::current_num_threads());
// Verify FFmpeg availability
if !args.ffmpeg_path.exists() && args.ffmpeg_path.to_str() == Some("ffmpeg") {
// Try to find ffmpeg in PATH
if Command::new("ffmpeg").arg("-version").output().is_err() {
anyhow::bail!("FFmpeg not found. Please install FFmpeg or specify path with --ffmpeg-path");
}
} else if !args.ffmpeg_path.exists() {
anyhow::bail!("FFmpeg not found at: {}", args.ffmpeg_path.display());
}
if args.benchmark {
// Benchmark mode
let video_path = args.input.clone()
.ok_or_else(|| anyhow::anyhow!("Benchmark requires input video file --input <path>"))?;
if !video_path.exists() {
anyhow::bail!("Video file not found: {}", video_path.display());
}
run_benchmark_suite(&video_path, &args.output, &args.ffmpeg_path, &args)?;
} else {
// Single processing mode
let video_path = args.input
.ok_or_else(|| anyhow::anyhow!("Please specify input video file --input <path>"))?;
if !video_path.exists() {
anyhow::bail!("Video file not found: {}", video_path.display());
}
// Run single keyframe extraction
let result = run_performance_test(
&video_path,
args.threshold,
"Single Processing",
&args.ffmpeg_path,
args.max_frames,
args.use_simd,
args.block_size,
args.verbose,
)?;
// Extract and save keyframes
let (frames, _, _) = extract_frames_memory_stream(&video_path, &args.ffmpeg_path, args.max_frames, args.verbose)?;
let keyframe_indices = extract_keyframes_optimized(&frames, args.threshold, args.use_simd, args.block_size, args.verbose)?;
let saved_count = save_keyframes_optimized(&video_path, &keyframe_indices, &args.output, &args.ffmpeg_path, args.max_save, args.verbose)?;
println!("\n✅ Processing Complete!");
println!("🎯 Keyframes extracted: {}", result.keyframes_extracted);
println!("💾 Keyframes saved: {}", saved_count);
println!("⚡ Processing speed: {:.1} FPS", result.processing_fps);
println!("📁 Output directory: {}", args.output.display());
// Save processing report
let timestamp = Local::now().format("%Y%m%d_%H%M%S").to_string();
let report_file = args.output.join(format!("processing_report_{}.json", timestamp));
let json_result = serde_json::to_string_pretty(&result)?;
fs::write(&report_file, json_result)?;
if args.verbose {
println!("📄 Processing report saved to: {}", report_file.display());
}
}
Ok(())
}

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@@ -0,0 +1,219 @@
#!/usr/bin/env python3
"""
启动脚本
支持开发模式和生产模式启动
"""
import os
import sys
import subprocess
import argparse
from pathlib import Path
from config import config
def check_rust_executable():
"""检查 Rust 可执行文件是否存在"""
rust_config = config.get("rust")
executable_name = rust_config.get("executable_name", "video_keyframe_extractor")
executable_path = rust_config.get("executable_path", "target/release")
possible_paths = [
f"./{executable_path}/{executable_name}.exe",
f"./{executable_path}/{executable_name}",
f"./{executable_name}.exe",
f"./{executable_name}"
]
for path in possible_paths:
if Path(path).exists():
print(f"✓ Found Rust executable: {path}")
return str(Path(path).absolute())
print("⚠ Warning: Rust executable not found")
print("Please compile first: cargo build --release")
return None
def check_dependencies():
"""检查 Python 依赖"""
try:
import fastapi
import uvicorn
print("✓ FastAPI dependencies available")
return True
except ImportError as e:
print(f"✗ Missing dependencies: {e}")
print("Please install: pip install -r requirements.txt")
return False
def install_dependencies():
"""安装依赖"""
print("Installing dependencies...")
try:
subprocess.run([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"],
check=True)
print("✓ Dependencies installed successfully")
return True
except subprocess.CalledProcessError as e:
print(f"✗ Failed to install dependencies: {e}")
return False
def start_development_server(host="127.0.0.1", port=8050, reload=True):
"""启动开发服务器"""
print(f" Starting development server on http://{host}:{port}")
print(f" API docs: http://{host}:{port}/docs")
print(f" Health check: http://{host}:{port}/health")
try:
import uvicorn
uvicorn.run(
"api_server:app",
host=host,
port=port,
reload=reload,
log_level="info"
)
except ImportError:
print("uvicorn not found, trying with subprocess...")
subprocess.run([
sys.executable, "-m", "uvicorn",
"api_server:app",
"--host", host,
"--port", str(port),
"--reload" if reload else ""
])
def start_production_server(host="0.0.0.0", port=8000, workers=4):
"""启动生产服务器"""
print(f"🚀 Starting production server on http://{host}:{port}")
print(f"Workers: {workers}")
subprocess.run([
sys.executable, "-m", "uvicorn",
"api_server:app",
"--host", host,
"--port", str(port),
"--workers", str(workers),
"--log-level", "warning"
])
def create_systemd_service():
"""创建 systemd 服务文件"""
current_dir = Path.cwd()
python_path = sys.executable
service_content = f"""[Unit]
Description=Video Keyframe Extraction API Server
After=network.target
[Service]
Type=exec
User=www-data
WorkingDirectory={current_dir}
Environment=PATH=/usr/bin:/usr/local/bin
ExecStart={python_path} -m uvicorn api_server:app --host 0.0.0.0 --port 8000 --workers 4
Restart=always
RestartSec=10
[Install]
WantedBy=multi-user.target
"""
service_file = Path("/etc/systemd/system/video-keyframe-api.service")
try:
with open(service_file, 'w') as f:
f.write(service_content)
print(f"✓ Systemd service created: {service_file}")
print("To enable and start:")
print(" sudo systemctl enable video-keyframe-api")
print(" sudo systemctl start video-keyframe-api")
except PermissionError:
print("✗ Permission denied. Please run with sudo for systemd service creation")
# 创建本地副本
local_service = Path("./video-keyframe-api.service")
with open(local_service, 'w') as f:
f.write(service_content)
print(f"✓ Service file created locally: {local_service}")
print(f"To install: sudo cp {local_service} /etc/systemd/system/")
def main():
parser = argparse.ArgumentParser(description="Video Keyframe Extraction API Server")
# 从配置文件获取默认值
server_config = config.get_server_config()
parser.add_argument("--mode", choices=["dev", "prod", "install"], default="dev",
help="运行模式: dev (开发), prod (生产), install (安装依赖)")
parser.add_argument("--host", default=server_config.get("host", "127.0.0.1"), help="绑定主机")
parser.add_argument("--port", type=int, default=server_config.get("port", 8000), help="端口号")
parser.add_argument("--workers", type=int, default=server_config.get("workers", 4), help="生产模式工作进程数")
parser.add_argument("--no-reload", action="store_true", help="禁用自动重载")
parser.add_argument("--check", action="store_true", help="仅检查环境")
parser.add_argument("--create-service", action="store_true", help="创建 systemd 服务")
args = parser.parse_args()
print("=== Video Keyframe Extraction API Server ===")
# 检查环境
rust_exe = check_rust_executable()
deps_ok = check_dependencies()
if args.check:
print("\n=== Environment Check ===")
print(f"Rust executable: {'' if rust_exe else ''}")
print(f"Python dependencies: {'' if deps_ok else ''}")
return
if args.create_service:
create_systemd_service()
return
# 安装模式
if args.mode == "install":
if not deps_ok:
install_dependencies()
else:
print("✓ Dependencies already installed")
return
# 检查必要条件
if not rust_exe:
print("✗ Cannot start without Rust executable")
print("Please run: cargo build --release")
sys.exit(1)
if not deps_ok:
print("Installing missing dependencies...")
if not install_dependencies():
sys.exit(1)
# 启动服务器
if args.mode == "dev":
start_development_server(
host=args.host,
port=args.port,
reload=not args.no_reload
)
elif args.mode == "prod":
start_production_server(
host=args.host,
port=args.port,
workers=args.workers
)
if __name__ == "__main__":
main()