fix: 添加工作流
This commit is contained in:
root
@@ -11,6 +11,7 @@ import typer
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# 导入命令模块
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from python_core.cli.commands import scene_app
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from python_core.cli.commands.jsonrpc_server import jsonrpc_app
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app = typer.Typer(
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name="mixvideo",
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@@ -28,13 +29,15 @@ app = typer.Typer(
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mixvideo scene batch-detect /videos # 批量检测
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mixvideo scene split video.mp4 # 分割视频
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mixvideo scene info video.mp4 # 视频信息
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mixvideo jsonrpc start # 启动JSON-RPC服务器
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""",
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rich_markup_mode="rich",
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no_args_is_help=True
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)
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# 添加场景检测命令组到主应用
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# 添加命令组到主应用
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app.add_typer(scene_app, name="scene")
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app.add_typer(jsonrpc_app, name="jsonrpc")
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@app.command()
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def init():
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303
python_core/cli/commands/jsonrpc_server.py
Normal file
303
python_core/cli/commands/jsonrpc_server.py
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@@ -0,0 +1,303 @@
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#!/usr/bin/env python3
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"""
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JSON-RPC Server Command
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JSON-RPC 服务器命令
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Provides HTTP and WebSocket JSON-RPC server for scene detection services.
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"""
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from pathlib import Path
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from typing import Optional
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import signal
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import sys
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import typer
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from python_core.cli.const import console
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from python_core.utils.jsonrpc_server import JSONRPCServer, JSONRPCWebSocketServer, ServerConfig
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# 创建子应用
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jsonrpc_app = typer.Typer(help="🌐 JSON-RPC 服务器")
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@jsonrpc_app.command()
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def start(
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host: str = typer.Option("localhost", help="🌐 服务器主机地址"),
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port: int = typer.Option(8080, help="🔌 服务器端口"),
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debug: bool = typer.Option(False, help="🐛 启用调试模式"),
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cors: bool = typer.Option(True, help="🔗 启用CORS支持"),
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websocket: bool = typer.Option(False, help="🔌 启用WebSocket服务器"),
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max_request_size: int = typer.Option(1024*1024, help="📦 最大请求大小（字节）")
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):
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"""🚀 启动JSON-RPC服务器"""
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config = ServerConfig(
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host=host,
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port=port,
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debug=debug,
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cors_enabled=cors,
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max_request_size=max_request_size
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)
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console.print(f"🚀 [bold blue]启动JSON-RPC服务器[/bold blue]")
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console.print(f"📍 地址: {host}:{port}")
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console.print(f"🔧 模式: {'WebSocket' if websocket else 'HTTP'}")
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console.print(f"🐛 调试: {'启用' if debug else '禁用'}")
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console.print(f"🔗 CORS: {'启用' if cors else '禁用'}")
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# 导入并注册所有JSON-RPC方法
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console.print("📋 注册JSON-RPC方法...")
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try:
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# 导入场景检测模块以注册方法
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from python_core.cli.scene_detect import detector
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console.print("✅ 场景检测方法已注册")
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except Exception as e:
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console.print(f"⚠️ 注册方法时出错: {e}")
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try:
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if websocket:
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# WebSocket服务器
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import asyncio
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server = JSONRPCWebSocketServer(config)
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# 注册信号处理
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def signal_handler(sig, frame):
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console.print("\n🛑 [yellow]收到停止信号，正在关闭服务器...[/yellow]")
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sys.exit(0)
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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# 启动异步服务器
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asyncio.run(server.start())
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else:
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# HTTP服务器
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server = JSONRPCServer(config)
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# 注册信号处理
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def signal_handler(sig, frame):
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console.print("\n🛑 [yellow]收到停止信号，正在关闭服务器...[/yellow]")
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server.stop()
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sys.exit(0)
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signal.signal(signal.SIGINT, signal_handler)
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signal.signal(signal.SIGTERM, signal_handler)
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# 启动服务器
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server.start(blocking=True)
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except Exception as e:
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console.print(f"❌ [red]服务器启动失败: {e}[/red]")
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raise typer.Exit(1)
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@jsonrpc_app.command()
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def test(
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host: str = typer.Option("localhost", help="🌐 服务器主机地址"),
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port: int = typer.Option(8080, help="🔌 服务器端口"),
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method: str = typer.Option("scene.detect", help="🎯 测试方法名"),
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video_path: Optional[str] = typer.Option(None, help="📹 测试视频路径")
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):
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"""🧪 测试JSON-RPC服务器"""
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import requests
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import json
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if not video_path:
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video_path = "assets/1/1752032011698.mp4" # 默认测试视频
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console.print(f"🧪 [bold blue]测试JSON-RPC服务器[/bold blue]")
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console.print(f"📍 服务器: http://{host}:{port}")
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console.print(f"🎯 方法: {method}")
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console.print(f"📹 视频: {video_path}")
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# 准备测试请求
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test_requests = {
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"scene.detect": {
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"video_path": video_path,
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"detector_type": "content",
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"threshold": 30.0,
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"min_scene_length": 1.0
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},
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"scene.get_video_info": {
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"video_path": video_path
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},
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"scene.detect_workflow": {
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"video_path": video_path,
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"detector_type": "content",
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"threshold": 15.0,
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"enable_ai_analysis": False
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}
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}
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params = test_requests.get(method, {"video_path": video_path})
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payload = {
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"jsonrpc": "2.0",
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"method": method,
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"params": params,
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"id": 1
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}
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try:
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console.print("📤 发送请求...")
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response = requests.post(
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f"http://{host}:{port}",
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json=payload,
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headers={'Content-Type': 'application/json'},
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timeout=30
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)
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console.print(f"📥 响应状态: {response.status_code}")
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if response.status_code == 200:
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result = response.json()
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console.print("✅ [green]请求成功[/green]")
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console.print("📋 响应内容:")
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console.print(json.dumps(result, indent=2, ensure_ascii=False))
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else:
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console.print(f"❌ [red]请求失败: {response.text}[/red]")
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except requests.exceptions.ConnectionError:
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console.print(f"❌ [red]无法连接到服务器 http://{host}:{port}[/red]")
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console.print("💡 请确保服务器已启动")
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raise typer.Exit(1)
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except Exception as e:
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console.print(f"❌ [red]测试失败: {e}[/red]")
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raise typer.Exit(1)
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@jsonrpc_app.command()
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def methods():
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"""📋 列出可用的JSON-RPC方法"""
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console.print("📋 [bold blue]可用的JSON-RPC方法[/bold blue]")
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console.print("=" * 60)
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methods_info = [
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{
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"method": "scene.detect",
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"description": "基础场景检测",
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"params": ["video_path", "detector_type?", "threshold?", "min_scene_length?"]
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},
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{
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"method": "scene.detect_workflow",
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"description": "LangGraph工作流场景检测",
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"params": ["video_path", "detector_type?", "threshold?", "min_scene_length?",
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"output_path?", "output_format?", "enable_ai_analysis?"]
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},
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{
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"method": "scene.get_video_info",
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"description": "获取视频信息",
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"params": ["video_path"]
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},
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{
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"method": "scene.batch_detect",
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"description": "批量场景检测",
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"params": ["directory", "detector_type?", "threshold?", "min_scene_length?",
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"output_dir?", "output_format?"]
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}
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]
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for info in methods_info:
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console.print(f"\n🎯 [bold]{info['method']}[/bold]")
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console.print(f" 📝 {info['description']}")
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console.print(f" 📋 参数: {', '.join(info['params'])}")
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console.print("\n💡 [yellow]参数说明:[/yellow]")
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console.print(" • ? 表示可选参数")
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console.print(" • detector_type: content/threshold/adaptive")
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console.print(" • output_format: json/csv/txt")
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console.print(" • threshold: 0-100")
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@jsonrpc_app.command()
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def client_example():
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"""📖 显示客户端调用示例"""
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console.print("📖 [bold blue]JSON-RPC 客户端调用示例[/bold blue]")
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console.print("=" * 60)
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examples = [
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{
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"title": "Python requests 示例",
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"code": '''import requests
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import json
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def call_scene_detect(video_path, threshold=30.0):
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payload = {
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"jsonrpc": "2.0",
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"method": "scene.detect",
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"params": {
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"video_path": video_path,
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"threshold": threshold
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},
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"id": 1
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}
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response = requests.post(
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"http://localhost:8080",
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json=payload,
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headers={'Content-Type': 'application/json'}
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)
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return response.json()
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# 调用示例
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result = call_scene_detect("video.mp4", 15.0)
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print(result)'''
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},
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{
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"title": "curl 命令示例",
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"code": '''curl -X POST http://localhost:8080 \\
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-H "Content-Type: application/json" \\
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-d '{
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"jsonrpc": "2.0",
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"method": "scene.detect",
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"params": {
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"video_path": "video.mp4",
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"threshold": 15.0
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},
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"id": 1
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}'
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'''
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},
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{
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"title": "JavaScript fetch 示例",
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"code": '''async function detectScenes(videoPath, threshold = 30.0) {
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const response = await fetch('http://localhost:8080', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({
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jsonrpc: '2.0',
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method: 'scene.detect',
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params: {
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video_path: videoPath,
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threshold: threshold
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},
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id: 1
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})
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});
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return await response.json();
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}
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// 调用示例
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detectScenes('video.mp4', 15.0).then(result => {
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console.log(result);
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});'''
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}
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]
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for example in examples:
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console.print(f"\n📝 [bold]{example['title']}[/bold]")
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console.print("```")
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console.print(example['code'])
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console.print("```")
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if __name__ == "__main__":
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jsonrpc_app()
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@@ -19,7 +19,9 @@ def detect(
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threshold: float = typer.Option(30.0, help="🎚️ 检测阈值 (0-100)"),
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min_scene_length: float = typer.Option(1.0, help="⏱️ 最小场景长度（秒）"),
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output: Optional[Path] = typer.Option(None, "--output", "-o", help="📄 输出文件路径"),
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format: str = typer.Option("json", help="📋 输出格式 (json/csv/txt)")
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format: str = typer.Option("json", help="📋 输出格式 (json/csv/txt)"),
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use_workflow: bool = typer.Option(False, "--workflow", help="🔄 使用LangGraph工作流"),
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enable_ai: bool = typer.Option(True, "--ai/--no-ai", help="🧠 启用AI分析")
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):
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"""🎯 检测单个视频的场景"""
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try:
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@@ -40,39 +42,97 @@ def detect(
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progress_reporter.info("💡 可用格式: json, csv, txt")
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raise typer.Exit(1)
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# 执行检测
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result = scene_detector.detect_scenes(
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video_path, detector_type, threshold, min_scene_length
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)
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if not result.success:
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progress_reporter.error(f"❌ 检测失败: {result.error}")
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raise typer.Exit(1)
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# 显示结果摘要
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console.print(f"📊 检测结果摘要:")
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console.print(f" 文件: {result.filename}")
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console.print(f" 检测器: {result.detector_type}")
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console.print(f" 阈值: {result.threshold}")
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console.print(f" 场景数: {result.total_scenes}")
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console.print(f" 总时长: {result.total_duration:.2f}秒")
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console.print(f" 检测时间: {result.detection_time:.2f}秒")
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# 显示场景详情
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if result.scenes:
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console.print(f"\n🎬 场景列表:")
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for scene in result.scenes[:10]: # 只显示前10个场景
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console.print(f" 场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s ({scene.duration:.2f}s)")
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if len(result.scenes) > 10:
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console.print(f" ... 还有 {len(result.scenes) - 10} 个场景")
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# 保存结果
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if output:
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scene_detector.save_results(result, output, output_format)
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progress_reporter.success(f"📄 结果已保存到: {output}")
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return result
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# 选择执行方式
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if use_workflow:
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# 使用LangGraph工作流
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progress_reporter.info("🔄 使用LangGraph工作流进行检测...")
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workflow_result = scene_detector.detect_with_workflow(
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video_path, detector_type, threshold, min_scene_length,
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output, output_format, enable_ai
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)
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result = workflow_result.get("detection_result")
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ai_analysis = workflow_result.get("ai_analysis")
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video_info = workflow_result.get("video_info")
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errors = workflow_result.get("errors", [])
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if errors:
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for error in errors:
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progress_reporter.error(f"❌ {error}")
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raise typer.Exit(1)
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if not result or not result.success:
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progress_reporter.error(f"❌ 工作流检测失败: {result.error if result else '未知错误'}")
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raise typer.Exit(1)
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# 显示工作流结果
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console.print(f"🔄 LangGraph工作流检测完成")
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console.print(f"📊 检测结果摘要:")
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console.print(f" 文件: {result.filename}")
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console.print(f" 检测器: {result.detector_type}")
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console.print(f" 阈值: {result.threshold}")
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console.print(f" 场景数: {result.total_scenes}")
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console.print(f" 总时长: {result.total_duration:.2f}秒")
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console.print(f" 检测时间: {result.detection_time:.2f}秒")
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# 显示视频信息
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if video_info:
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console.print(f"\n📹 视频信息:")
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console.print(f" 分辨率: {video_info.get('resolution', 'Unknown')}")
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console.print(f" 帧率: {video_info.get('fps', 0):.2f} fps")
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console.print(f" 总帧数: {video_info.get('frame_count', 0)}")
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# 显示AI分析结果
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if ai_analysis and enable_ai:
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console.print(f"\n🧠 AI分析结果:")
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console.print(f"{ai_analysis}")
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# 显示场景详情
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if result.scenes:
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console.print(f"\n🎬 场景列表:")
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for scene in result.scenes[:10]:
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console.print(f" 场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s ({scene.duration:.2f}s)")
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if len(result.scenes) > 10:
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console.print(f" ... 还有 {len(result.scenes) - 10} 个场景")
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return workflow_result
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else:
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# 使用传统方法
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result = scene_detector.detect_scenes(
|
||||
video_path, detector_type, threshold, min_scene_length
|
||||
)
|
||||
|
||||
if not result.success:
|
||||
progress_reporter.error(f"❌ 检测失败: {result.error}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
# 显示结果摘要
|
||||
console.print(f"📊 检测结果摘要:")
|
||||
console.print(f" 文件: {result.filename}")
|
||||
console.print(f" 检测器: {result.detector_type}")
|
||||
console.print(f" 阈值: {result.threshold}")
|
||||
console.print(f" 场景数: {result.total_scenes}")
|
||||
console.print(f" 总时长: {result.total_duration:.2f}秒")
|
||||
console.print(f" 检测时间: {result.detection_time:.2f}秒")
|
||||
|
||||
# 显示场景详情
|
||||
if result.scenes:
|
||||
console.print(f"\n🎬 场景列表:")
|
||||
for scene in result.scenes[:10]: # 只显示前10个场景
|
||||
console.print(f" 场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s ({scene.duration:.2f}s)")
|
||||
|
||||
if len(result.scenes) > 10:
|
||||
console.print(f" ... 还有 {len(result.scenes) - 10} 个场景")
|
||||
|
||||
# 保存结果
|
||||
if output:
|
||||
scene_detector.save_results(result, output, output_format)
|
||||
progress_reporter.success(f"📄 结果已保存到: {output}")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
progress_reporter.error(f"❌ 命令执行失败: {e}")
|
||||
@@ -342,3 +402,148 @@ def info(
|
||||
except Exception as e:
|
||||
progress_reporter.error(f"❌ 获取视频信息失败: {e}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
@scene_app.command()
|
||||
def workflow(
|
||||
video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True),
|
||||
detector: str = typer.Option("content", help="🔧 检测器类型 (content/threshold/adaptive)"),
|
||||
threshold: float = typer.Option(30.0, help="🎚️ 检测阈值 (0-100)"),
|
||||
min_scene_length: float = typer.Option(1.0, help="⏱️ 最小场景长度（秒）"),
|
||||
output: Optional[Path] = typer.Option(None, "--output", "-o", help="📄 输出文件路径"),
|
||||
format: str = typer.Option("json", help="📋 输出格式 (json/csv/txt)"),
|
||||
enable_ai: bool = typer.Option(True, "--ai/--no-ai", help="🧠 启用AI分析"),
|
||||
interactive: bool = typer.Option(False, "--interactive", "-i", help="🔄 交互式工作流")
|
||||
):
|
||||
"""🔄 使用LangGraph工作流进行智能场景检测"""
|
||||
try:
|
||||
from python_core.cli.scene_detect import detector as scene_detector, DetectorType, OutputFormat
|
||||
|
||||
# 验证参数
|
||||
try:
|
||||
detector_type = DetectorType(detector)
|
||||
output_format = OutputFormat(format)
|
||||
except ValueError as e:
|
||||
progress_reporter.error(f"❌ 参数错误: {e}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
console.print("🔄 [bold blue]LangGraph智能场景检测工作流[/bold blue]")
|
||||
console.print("=" * 60)
|
||||
|
||||
if interactive:
|
||||
# 交互式模式
|
||||
console.print("🎯 交互式模式启动...")
|
||||
|
||||
# 确认参数
|
||||
console.print(f"\n📋 检测参数:")
|
||||
console.print(f" 视频文件: {video_path}")
|
||||
console.print(f" 检测器: {detector}")
|
||||
console.print(f" 阈值: {threshold}")
|
||||
console.print(f" 最小场景长度: {min_scene_length}秒")
|
||||
console.print(f" AI分析: {'启用' if enable_ai else '禁用'}")
|
||||
|
||||
if not typer.confirm("\n是否继续执行？"):
|
||||
console.print("❌ 用户取消操作")
|
||||
return
|
||||
|
||||
# 执行工作流
|
||||
progress_reporter.info("🚀 启动LangGraph工作流...")
|
||||
|
||||
workflow_result = scene_detector.detect_with_workflow(
|
||||
video_path, detector_type, threshold, min_scene_length,
|
||||
output, output_format, enable_ai
|
||||
)
|
||||
|
||||
result = workflow_result.get("detection_result")
|
||||
ai_analysis = workflow_result.get("ai_analysis")
|
||||
video_info = workflow_result.get("video_info")
|
||||
workflow_state = workflow_result.get("workflow_state")
|
||||
errors = workflow_result.get("errors", [])
|
||||
|
||||
# 检查错误
|
||||
if errors:
|
||||
console.print("\n❌ [red]工作流执行中发现错误:[/red]")
|
||||
for error in errors:
|
||||
console.print(f" • {error}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
if not result or not result.success:
|
||||
progress_reporter.error(f"❌ 工作流检测失败: {result.error if result else '未知错误'}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
# 显示完整结果
|
||||
console.print("\n" + "=" * 60)
|
||||
console.print("🎉 [bold green]LangGraph工作流执行完成[/bold green]")
|
||||
console.print("=" * 60)
|
||||
|
||||
# 工作流状态
|
||||
console.print(f"\n🔄 工作流状态: [bold]{workflow_state}[/bold]")
|
||||
|
||||
# 视频信息
|
||||
if video_info:
|
||||
console.print(f"\n📹 [bold]视频信息[/bold]:")
|
||||
console.print(f" 文件名: {result.filename}")
|
||||
console.print(f" 分辨率: {video_info.get('resolution', 'Unknown')}")
|
||||
console.print(f" 帧率: {video_info.get('fps', 0):.2f} fps")
|
||||
console.print(f" 总帧数: {video_info.get('frame_count', 0):,}")
|
||||
console.print(f" 时长: {result.total_duration:.2f}秒")
|
||||
|
||||
# 检测结果
|
||||
console.print(f"\n🎯 [bold]检测结果[/bold]:")
|
||||
console.print(f" 检测器类型: {result.detector_type}")
|
||||
console.print(f" 检测阈值: {result.threshold}")
|
||||
console.print(f" 场景数量: [bold green]{result.total_scenes}[/bold green]")
|
||||
console.print(f" 检测耗时: {result.detection_time:.2f}秒")
|
||||
|
||||
# 场景详情
|
||||
if result.scenes:
|
||||
console.print(f"\n🎬 [bold]场景详情[/bold]:")
|
||||
for scene in result.scenes:
|
||||
duration_color = "green" if scene.duration >= 2.0 else "yellow" if scene.duration >= 1.0 else "red"
|
||||
console.print(
|
||||
f" 场景 {scene.index:2d}: "
|
||||
f"{scene.start_time:6.2f}s - {scene.end_time:6.2f}s "
|
||||
f"([{duration_color}]{scene.duration:5.2f}s[/{duration_color}])"
|
||||
)
|
||||
|
||||
# AI分析结果
|
||||
if ai_analysis and enable_ai:
|
||||
console.print(f"\n🧠 [bold]AI智能分析[/bold]:")
|
||||
console.print("-" * 50)
|
||||
console.print(ai_analysis)
|
||||
console.print("-" * 50)
|
||||
elif enable_ai:
|
||||
console.print(f"\n⚠️ AI分析不可用")
|
||||
|
||||
# 保存信息
|
||||
if output:
|
||||
console.print(f"\n💾 结果已保存到: [bold]{output}[/bold]")
|
||||
|
||||
# 交互式后续操作
|
||||
if interactive:
|
||||
console.print(f"\n🎯 [bold]后续操作选项[/bold]:")
|
||||
console.print("1. 保存结果到文件")
|
||||
console.print("2. 调整参数重新检测")
|
||||
console.print("3. 分割视频")
|
||||
console.print("4. 退出")
|
||||
|
||||
choice = typer.prompt("请选择操作 (1-4)", type=int, default=4)
|
||||
|
||||
if choice == 1 and not output:
|
||||
output_path = typer.prompt("请输入输出文件路径", type=str)
|
||||
scene_detector.save_results(result, Path(output_path), output_format)
|
||||
console.print(f"✅ 结果已保存到: {output_path}")
|
||||
|
||||
elif choice == 2:
|
||||
console.print("🔄 参数调整功能开发中...")
|
||||
|
||||
elif choice == 3:
|
||||
console.print("✂️ 视频分割功能开发中...")
|
||||
|
||||
else:
|
||||
console.print("👋 感谢使用LangGraph工作流！")
|
||||
|
||||
return workflow_result
|
||||
|
||||
except Exception as e:
|
||||
progress_reporter.error(f"❌ 工作流命令执行失败: {e}")
|
||||
raise typer.Exit(1)
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
from python_core.utils.jsonrpc import create_progress_reporter
|
||||
from python_core.utils.jsonrpc_enhanced import create_progress_reporter
|
||||
from rich.console import Console
|
||||
from python_core.config import settings
|
||||
console = Console()
|
||||
|
||||
@@ -1,23 +1,26 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
PySceneDetect 场景检测命令行工具
|
||||
PySceneDetect 场景检测命令行工具 - LangGraph增强版
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Optional, List
|
||||
from typing import Optional, List, Literal, Dict, Any
|
||||
from enum import Enum
|
||||
from dataclasses import dataclass, asdict
|
||||
|
||||
import typer
|
||||
from python_core.cli.const import progress_reporter, console, project_root
|
||||
from python_core.cli.const import progress_reporter
|
||||
|
||||
# 检查 PySceneDetect 依赖
|
||||
# PySceneDetect 依赖
|
||||
from scenedetect import open_video, SceneManager
|
||||
from scenedetect.detectors import ContentDetector, ThresholdDetector
|
||||
from scenedetect.video_splitter import split_video_ffmpeg
|
||||
|
||||
# LangGraph 依赖
|
||||
from langgraph.graph import StateGraph, START, END
|
||||
from langgraph.graph.state import CompiledStateGraph
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
|
||||
class DetectorType(str, Enum):
|
||||
"""检测器类型"""
|
||||
@@ -55,11 +58,59 @@ class DetectionResult:
|
||||
success: bool
|
||||
error: Optional[str] = None
|
||||
|
||||
# LangGraph 工作流状态
|
||||
@dataclass
|
||||
class SceneDetectionWorkflowState:
|
||||
"""场景检测工作流状态"""
|
||||
# 输入参数
|
||||
video_path: str = ""
|
||||
detector_type: str = "content"
|
||||
threshold: float = 30.0
|
||||
min_scene_length: float = 1.0
|
||||
output_path: Optional[str] = None
|
||||
output_format: str = "json"
|
||||
enable_ai_analysis: bool = True
|
||||
|
||||
# 工作流状态
|
||||
current_stage: str = "init"
|
||||
progress: int = 0
|
||||
total_steps: int = 5
|
||||
|
||||
# 中间结果
|
||||
video_info: Dict[str, Any] = None
|
||||
raw_scenes: List[Any] = None
|
||||
processed_scenes: List[SceneInfo] = None
|
||||
|
||||
# 最终结果
|
||||
detection_result: Optional[DetectionResult] = None
|
||||
ai_analysis: Optional[str] = None
|
||||
|
||||
# 错误处理
|
||||
errors: List[str] = None
|
||||
|
||||
def __post_init__(self):
|
||||
if self.video_info is None:
|
||||
self.video_info = {}
|
||||
if self.raw_scenes is None:
|
||||
self.raw_scenes = []
|
||||
if self.processed_scenes is None:
|
||||
self.processed_scenes = []
|
||||
if self.errors is None:
|
||||
self.errors = []
|
||||
|
||||
class SceneDetector:
|
||||
"""场景检测器"""
|
||||
|
||||
|
||||
def __init__(self):
|
||||
self.supported_formats = {'.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm', '.m4v'}
|
||||
|
||||
# 初始化AI分析器（如果可用）
|
||||
try:
|
||||
self.llm = ChatAnthropic(model="claude-3-5-sonnet-20241022")
|
||||
self.ai_enabled = True
|
||||
except Exception as e:
|
||||
progress_reporter.warning(f"⚠️ AI分析器初始化失败: {e}")
|
||||
self.ai_enabled = False
|
||||
|
||||
def detect_scenes(self, video_path: Path, detector_type: DetectorType = DetectorType.CONTENT,
|
||||
threshold: float = 30.0, min_scene_length: float = 1.0) -> DetectionResult:
|
||||
@@ -361,5 +412,460 @@ class SceneDetector:
|
||||
|
||||
f.write("\n")
|
||||
|
||||
# ==================== LangGraph 工作流方法 ====================
|
||||
|
||||
def create_detection_workflow(self) -> Optional[CompiledStateGraph]:
|
||||
"""创建场景检测工作流"""
|
||||
|
||||
# 定义工作流节点
|
||||
def validate_input(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""验证输入参数"""
|
||||
progress_reporter.info("🔍 验证输入参数...")
|
||||
|
||||
video_path = Path(state.video_path)
|
||||
errors = []
|
||||
|
||||
# 验证文件存在
|
||||
if not video_path.exists():
|
||||
errors.append(f"视频文件不存在: {video_path}")
|
||||
|
||||
# 验证文件格式
|
||||
if video_path.suffix.lower() not in self.supported_formats:
|
||||
errors.append(f"不支持的文件格式: {video_path.suffix}")
|
||||
|
||||
# 验证参数范围
|
||||
if not (0 <= state.threshold <= 100):
|
||||
errors.append(f"阈值超出范围 (0-100): {state.threshold}")
|
||||
|
||||
if state.min_scene_length < 0:
|
||||
errors.append(f"最小场景长度不能为负数: {state.min_scene_length}")
|
||||
|
||||
return {
|
||||
"current_stage": "validated" if not errors else "error",
|
||||
"progress": 1,
|
||||
"errors": errors
|
||||
}
|
||||
|
||||
def extract_video_info(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""提取视频信息"""
|
||||
progress_reporter.info("📊 提取视频信息...")
|
||||
|
||||
try:
|
||||
import cv2
|
||||
cap = cv2.VideoCapture(state.video_path)
|
||||
|
||||
if not cap.isOpened():
|
||||
raise Exception("无法打开视频文件")
|
||||
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
duration = frame_count / fps if fps > 0 else 0
|
||||
|
||||
cap.release()
|
||||
|
||||
video_info = {
|
||||
"fps": fps,
|
||||
"frame_count": frame_count,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"duration": duration,
|
||||
"resolution": f"{width}x{height}"
|
||||
}
|
||||
|
||||
progress_reporter.info(f"📹 视频信息: {video_info['resolution']}, {fps:.2f}fps, {duration:.2f}s")
|
||||
|
||||
return {
|
||||
"current_stage": "info_extracted",
|
||||
"progress": 2,
|
||||
"video_info": video_info
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"current_stage": "error",
|
||||
"errors": state.errors + [f"提取视频信息失败: {e}"]
|
||||
}
|
||||
|
||||
def detect_scenes(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""执行场景检测"""
|
||||
progress_reporter.info("🎯 执行场景检测...")
|
||||
|
||||
try:
|
||||
# 使用现有的检测逻辑
|
||||
result = self.detect_scenes(
|
||||
Path(state.video_path),
|
||||
DetectorType(state.detector_type),
|
||||
state.threshold,
|
||||
state.min_scene_length
|
||||
)
|
||||
|
||||
return {
|
||||
"current_stage": "scenes_detected",
|
||||
"progress": 3,
|
||||
"detection_result": result,
|
||||
"processed_scenes": result.scenes
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"current_stage": "error",
|
||||
"errors": state.errors + [f"场景检测失败: {e}"]
|
||||
}
|
||||
|
||||
def analyze_with_ai(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""AI分析场景结果"""
|
||||
if not self.ai_enabled or not state.enable_ai_analysis:
|
||||
progress_reporter.info("⚠️ AI分析已禁用，跳过此步骤")
|
||||
return {
|
||||
"current_stage": "analysis_skipped",
|
||||
"progress": 4,
|
||||
"ai_analysis": "AI分析已禁用"
|
||||
}
|
||||
|
||||
progress_reporter.info("🧠 AI分析场景结果...")
|
||||
|
||||
try:
|
||||
result = state.detection_result
|
||||
video_info = state.video_info
|
||||
|
||||
analysis_prompt = f"""
|
||||
请分析以下视频场景检测结果：
|
||||
|
||||
视频信息:
|
||||
- 文件: {result.filename}
|
||||
- 分辨率: {video_info.get('resolution', 'Unknown')}
|
||||
- 时长: {result.total_duration:.2f}秒
|
||||
- 帧率: {video_info.get('fps', 0):.2f}fps
|
||||
|
||||
检测结果:
|
||||
- 检测器: {result.detector_type}
|
||||
- 阈值: {result.threshold}
|
||||
- 场景数: {result.total_scenes}
|
||||
- 检测时间: {result.detection_time:.2f}秒
|
||||
|
||||
场景详情:
|
||||
{self._format_scenes_for_ai(result.scenes)}
|
||||
|
||||
请提供:
|
||||
1. 场景分布分析
|
||||
2. 检测质量评估
|
||||
3. 参数优化建议
|
||||
4. 潜在问题识别
|
||||
"""
|
||||
|
||||
response = self.llm.invoke([{"role": "user", "content": analysis_prompt}])
|
||||
|
||||
return {
|
||||
"current_stage": "ai_analyzed",
|
||||
"progress": 4,
|
||||
"ai_analysis": response.content
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
progress_reporter.warning(f"⚠️ AI分析失败: {e}")
|
||||
return {
|
||||
"current_stage": "analysis_failed",
|
||||
"progress": 4,
|
||||
"ai_analysis": f"AI分析失败: {e}"
|
||||
}
|
||||
|
||||
def finalize_results(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""整理最终结果"""
|
||||
progress_reporter.info("📋 整理最终结果...")
|
||||
|
||||
# 保存结果（如果指定了输出路径）
|
||||
if state.output_path and state.detection_result:
|
||||
try:
|
||||
output_path = Path(state.output_path)
|
||||
output_format = OutputFormat(state.output_format)
|
||||
self.save_results(state.detection_result, output_path, output_format)
|
||||
except Exception as e:
|
||||
progress_reporter.warning(f"⚠️ 保存结果失败: {e}")
|
||||
|
||||
return {
|
||||
"current_stage": "completed",
|
||||
"progress": 5
|
||||
}
|
||||
|
||||
# 路由函数
|
||||
def route_next_step(state: SceneDetectionWorkflowState) -> Literal["extract_info", "detect", "analyze", "finalize", "error"]:
|
||||
if state.errors:
|
||||
return "error"
|
||||
elif state.current_stage == "validated":
|
||||
return "extract_info"
|
||||
elif state.current_stage == "info_extracted":
|
||||
return "detect"
|
||||
elif state.current_stage == "scenes_detected":
|
||||
return "analyze"
|
||||
elif state.current_stage in ["ai_analyzed", "analysis_skipped", "analysis_failed"]:
|
||||
return "finalize"
|
||||
else:
|
||||
return "error"
|
||||
|
||||
def handle_error(state: SceneDetectionWorkflowState) -> Dict[str, Any]:
|
||||
"""处理错误"""
|
||||
error_msg = "; ".join(state.errors)
|
||||
progress_reporter.error(f"❌ 工作流错误: {error_msg}")
|
||||
return {"current_stage": "failed"}
|
||||
|
||||
# 构建工作流图
|
||||
workflow = StateGraph(SceneDetectionWorkflowState)
|
||||
|
||||
# 添加节点
|
||||
workflow.add_node("validate", validate_input)
|
||||
workflow.add_node("extract_info", extract_video_info)
|
||||
workflow.add_node("detect", detect_scenes)
|
||||
workflow.add_node("analyze", analyze_with_ai)
|
||||
workflow.add_node("finalize", finalize_results)
|
||||
workflow.add_node("error", handle_error)
|
||||
|
||||
# 添加边
|
||||
workflow.add_edge(START, "validate")
|
||||
workflow.add_conditional_edges("validate", route_next_step)
|
||||
workflow.add_conditional_edges("extract_info", route_next_step)
|
||||
workflow.add_conditional_edges("detect", route_next_step)
|
||||
workflow.add_conditional_edges("analyze", route_next_step)
|
||||
workflow.add_edge("finalize", END)
|
||||
workflow.add_edge("error", END)
|
||||
|
||||
# 编译工作流
|
||||
memory = MemorySaver()
|
||||
return workflow.compile(checkpointer=memory)
|
||||
|
||||
def _format_scenes_for_ai(self, scenes: List[SceneInfo]) -> str:
|
||||
"""格式化场景信息供AI分析"""
|
||||
if not scenes:
|
||||
return "无场景数据"
|
||||
|
||||
formatted = []
|
||||
for scene in scenes[:10]: # 只显示前10个场景
|
||||
formatted.append(
|
||||
f"场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s "
|
||||
f"(时长: {scene.duration:.2f}s)"
|
||||
)
|
||||
|
||||
if len(scenes) > 10:
|
||||
formatted.append(f"... 还有 {len(scenes) - 10} 个场景")
|
||||
|
||||
return "\n".join(formatted)
|
||||
|
||||
def detect_with_workflow(self, video_path: Path, detector_type: DetectorType = DetectorType.CONTENT,
|
||||
threshold: float = 30.0, min_scene_length: float = 1.0,
|
||||
output_path: Optional[Path] = None, output_format: OutputFormat = OutputFormat.JSON,
|
||||
enable_ai_analysis: bool = True) -> Dict[str, Any]:
|
||||
"""使用LangGraph工作流进行场景检测"""
|
||||
|
||||
# 创建工作流
|
||||
workflow = self.create_detection_workflow()
|
||||
if not workflow:
|
||||
raise Exception("无法创建工作流")
|
||||
|
||||
# 初始化状态
|
||||
initial_state = SceneDetectionWorkflowState(
|
||||
video_path=str(video_path),
|
||||
detector_type=detector_type.value,
|
||||
threshold=threshold,
|
||||
min_scene_length=min_scene_length,
|
||||
output_path=str(output_path) if output_path else None,
|
||||
output_format=output_format.value,
|
||||
enable_ai_analysis=enable_ai_analysis # 使用参数
|
||||
)
|
||||
|
||||
# 执行工作流
|
||||
config = {"configurable": {"thread_id": f"detection_{int(time.time())}"}}
|
||||
|
||||
try:
|
||||
final_state = workflow.invoke(initial_state, config)
|
||||
|
||||
return {
|
||||
"detection_result": final_state.get("detection_result"),
|
||||
"ai_analysis": final_state.get("ai_analysis"),
|
||||
"video_info": final_state.get("video_info"),
|
||||
"workflow_state": final_state.get("current_stage"),
|
||||
"errors": final_state.get("errors", [])
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
progress_reporter.error(f"❌ 工作流执行失败: {e}")
|
||||
raise
|
||||
|
||||
# ==================== JSON-RPC 方法注册 ====================
|
||||
|
||||
def register_jsonrpc_methods(self):
|
||||
"""注册JSON-RPC方法到全局注册器"""
|
||||
from python_core.utils.jsonrpc_enhanced import method_registry
|
||||
|
||||
# 注册方法到全局注册器
|
||||
method_registry.register_function(self.jsonrpc_detect_scenes, "scene.detect")
|
||||
method_registry.register_function(self.jsonrpc_detect_with_workflow, "scene.detect_workflow")
|
||||
method_registry.register_function(self.jsonrpc_get_video_info, "scene.get_video_info")
|
||||
method_registry.register_function(self.jsonrpc_batch_detect, "scene.batch_detect")
|
||||
|
||||
def jsonrpc_detect_scenes(self, video_path: str, detector_type: str = "content",
|
||||
threshold: float = 30.0, min_scene_length: float = 1.0) -> Dict[str, Any]:
|
||||
"""JSON-RPC方法：场景检测"""
|
||||
try:
|
||||
result = self.detect_scenes(
|
||||
Path(video_path),
|
||||
DetectorType(detector_type),
|
||||
threshold,
|
||||
min_scene_length
|
||||
)
|
||||
|
||||
return {
|
||||
"success": result.success,
|
||||
"filename": result.filename,
|
||||
"detector_type": result.detector_type,
|
||||
"threshold": result.threshold,
|
||||
"total_scenes": result.total_scenes,
|
||||
"total_duration": result.total_duration,
|
||||
"detection_time": result.detection_time,
|
||||
"scenes": [asdict(scene) for scene in result.scenes],
|
||||
"error": result.error
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def jsonrpc_detect_with_workflow(self, video_path: str, detector_type: str = "content",
|
||||
threshold: float = 30.0, min_scene_length: float = 1.0,
|
||||
output_path: Optional[str] = None, output_format: str = "json",
|
||||
enable_ai_analysis: bool = True) -> Dict[str, Any]:
|
||||
"""JSON-RPC方法：工作流场景检测"""
|
||||
try:
|
||||
output_path_obj = Path(output_path) if output_path else None
|
||||
|
||||
result = self.detect_with_workflow(
|
||||
Path(video_path),
|
||||
DetectorType(detector_type),
|
||||
threshold,
|
||||
min_scene_length,
|
||||
output_path_obj,
|
||||
OutputFormat(output_format),
|
||||
enable_ai_analysis
|
||||
)
|
||||
|
||||
# 序列化结果
|
||||
serialized_result = {}
|
||||
for key, value in result.items():
|
||||
if key == "detection_result" and value:
|
||||
serialized_result[key] = {
|
||||
"success": value.success,
|
||||
"filename": value.filename,
|
||||
"detector_type": value.detector_type,
|
||||
"threshold": value.threshold,
|
||||
"total_scenes": value.total_scenes,
|
||||
"total_duration": value.total_duration,
|
||||
"detection_time": value.detection_time,
|
||||
"scenes": [asdict(scene) for scene in value.scenes],
|
||||
"error": value.error
|
||||
}
|
||||
else:
|
||||
serialized_result[key] = value
|
||||
|
||||
return serialized_result
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def get_video_info(self, video_path: Path) -> Dict[str, Any]:
|
||||
"""获取视频信息"""
|
||||
try:
|
||||
import cv2
|
||||
cap = cv2.VideoCapture(str(video_path))
|
||||
|
||||
if not cap.isOpened():
|
||||
raise Exception("无法打开视频文件")
|
||||
|
||||
fps = cap.get(cv2.CAP_PROP_FPS)
|
||||
frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
||||
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||||
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
duration = frame_count / fps if fps > 0 else 0
|
||||
|
||||
cap.release()
|
||||
|
||||
return {
|
||||
"filename": video_path.name,
|
||||
"fps": fps,
|
||||
"frame_count": frame_count,
|
||||
"width": width,
|
||||
"height": height,
|
||||
"duration": duration,
|
||||
"resolution": f"{width}x{height}",
|
||||
"file_size": video_path.stat().st_size if video_path.exists() else 0
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"获取视频信息失败: {e}")
|
||||
|
||||
def jsonrpc_get_video_info(self, video_path: str) -> Dict[str, Any]:
|
||||
"""JSON-RPC方法：获取视频信息"""
|
||||
try:
|
||||
info = self.get_video_info(Path(video_path))
|
||||
return {
|
||||
"success": True,
|
||||
"info": info
|
||||
}
|
||||
except Exception as e:
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
def jsonrpc_batch_detect(self, directory: str, detector_type: str = "content",
|
||||
threshold: float = 30.0, min_scene_length: float = 1.0,
|
||||
output_dir: Optional[str] = None, output_format: str = "json") -> Dict[str, Any]:
|
||||
"""JSON-RPC方法：批量场景检测"""
|
||||
try:
|
||||
output_dir_obj = Path(output_dir) if output_dir else None
|
||||
|
||||
results = self.batch_detect(
|
||||
Path(directory),
|
||||
DetectorType(detector_type),
|
||||
threshold,
|
||||
min_scene_length,
|
||||
output_dir_obj,
|
||||
OutputFormat(output_format)
|
||||
)
|
||||
|
||||
# 序列化结果
|
||||
serialized_results = []
|
||||
for result in results:
|
||||
serialized_results.append({
|
||||
"success": result.success,
|
||||
"filename": result.filename,
|
||||
"detector_type": result.detector_type,
|
||||
"threshold": result.threshold,
|
||||
"total_scenes": result.total_scenes,
|
||||
"total_duration": result.total_duration,
|
||||
"detection_time": result.detection_time,
|
||||
"scenes": [asdict(scene) for scene in result.scenes],
|
||||
"error": result.error
|
||||
})
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"results": serialized_results,
|
||||
"total_processed": len(results)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"success": False,
|
||||
"error": str(e)
|
||||
}
|
||||
|
||||
|
||||
# 创建全局检测器实例
|
||||
detector = SceneDetector()
|
||||
detector = SceneDetector()
|
||||
|
||||
# 注册JSON-RPC方法
|
||||
detector.register_jsonrpc_methods()
|
||||
Reference in New Issue
Block a user