fix: 修复命令行工具
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root
15
python_core/scene_detection/workflows/__init__.py
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15
python_core/scene_detection/workflows/__init__.py
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#!/usr/bin/env python3
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"""
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Scene Detection Workflows
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场景检测工作流
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导出所有工作流类
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"""
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from .workflow_manager import SceneDetectionWorkflowManager
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from .workflow_nodes import WorkflowNodes
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__all__ = [
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"SceneDetectionWorkflowManager",
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"WorkflowNodes"
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]
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176
python_core/scene_detection/workflows/workflow_manager.py
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176
python_core/scene_detection/workflows/workflow_manager.py
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#!/usr/bin/env python3
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"""
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Workflow Manager
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工作流管理器
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"""
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import time
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from pathlib import Path
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from typing import Dict, Any, Optional, Literal
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from python_core.utils.logger import logger
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from python_core.utils.jsonrpc_enhanced import EnhancedJSONRPCResponse
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from ..types import SceneDetectionWorkflowState, DetectorType, OutputFormat
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from .workflow_nodes import WorkflowNodes
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class SceneDetectionWorkflowManager:
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"""场景检测工作流管理器"""
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def __init__(self):
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self.nodes = WorkflowNodes()
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self.workflow = None
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def create_detection_workflow(self):
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"""创建检测工作流"""
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try:
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from langgraph.graph import StateGraph, END
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# 创建状态图
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workflow = StateGraph(SceneDetectionWorkflowState)
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# 添加节点
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workflow.add_node("validate", self.nodes.validate_input)
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workflow.add_node("extract_info", self.nodes.extract_video_info)
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workflow.add_node("detect", self.nodes.detect_scenes)
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workflow.add_node("analyze", self.nodes.analyze_with_ai)
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workflow.add_node("finalize", self.nodes.finalize_results)
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workflow.add_node("error", self.nodes.handle_error)
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# 设置入口点
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workflow.set_entry_point("validate")
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# 添加条件边
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workflow.add_conditional_edges(
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"validate",
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self._route_next_step,
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{
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"extract_info": "extract_info",
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"error": "error"
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}
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)
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workflow.add_conditional_edges(
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"extract_info",
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self._route_next_step,
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{
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"detect": "detect",
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"error": "error"
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}
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)
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workflow.add_conditional_edges(
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"detect",
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self._route_next_step,
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{
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"analyze": "analyze",
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"error": "error"
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}
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)
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workflow.add_conditional_edges(
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"analyze",
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self._route_next_step,
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{
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"finalize": "finalize",
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"error": "error"
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}
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)
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# 结束节点
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workflow.add_edge("finalize", END)
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workflow.add_edge("error", END)
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# 编译工作流
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self.workflow = workflow.compile()
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return self.workflow
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except ImportError:
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logger.error("❌ LangGraph未安装，无法创建工作流")
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return None
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except Exception as e:
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logger.error(f"❌ 创建工作流失败: {e}")
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return None
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def _route_next_step(self, state: SceneDetectionWorkflowState) -> Literal["extract_info", "detect", "analyze", "finalize", "error"]:
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"""路由下一步"""
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if state.errors:
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return "error"
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elif state.current_stage == "validated":
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return "extract_info"
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elif state.current_stage == "info_extracted":
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return "detect"
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elif state.current_stage == "scenes_detected":
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return "analyze"
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elif state.current_stage in ["ai_analyzed", "analysis_skipped", "analysis_failed"]:
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return "finalize"
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else:
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return "error"
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def detect_with_workflow(self, video_path: Path, detector_type: DetectorType = DetectorType.CONTENT,
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threshold: float = 30.0, min_scene_length: float = 1.0,
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output_path: Optional[Path] = None, output_format: OutputFormat = OutputFormat.JSON,
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enable_ai_analysis: bool = True, request_id: Optional[str] = None) -> Dict[str, Any]:
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"""使用LangGraph工作流进行场景检测"""
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# 创建工作流
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workflow = self.create_detection_workflow()
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if not workflow:
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raise Exception("无法创建工作流")
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# 初始化状态
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initial_state = SceneDetectionWorkflowState(
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video_path=str(video_path),
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detector_type=detector_type.value,
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threshold=threshold,
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min_scene_length=min_scene_length,
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output_path=str(output_path) if output_path else None,
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output_format=output_format.value,
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enable_ai_analysis=enable_ai_analysis,
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request_id=request_id,
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enable_jsonrpc=request_id is not None # 如果有request_id就启用JSON-RPC
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)
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# 执行工作流
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config = {"configurable": {"thread_id": f"detection_{int(time.time())}"}}
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try:
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final_state = workflow.invoke(initial_state, config)
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# 如果是JSON-RPC模式，结果已经通过JSON-RPC发送了
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if request_id is not None:
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# JSON-RPC模式：结果已经在工作流节点中发送
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# 这里返回简化的状态信息供内部使用
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return {
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"jsonrpc_mode": True,
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"request_id": request_id,
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"workflow_state": final_state.get("current_stage"),
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"final_result_sent": True
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}
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else:
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# 非JSON-RPC模式：返回完整结果
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return {
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"detection_result": final_state.get("detection_result"),
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"ai_analysis": final_state.get("ai_analysis"),
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"video_info": final_state.get("video_info"),
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"workflow_state": final_state.get("current_stage"),
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"errors": final_state.get("errors", [])
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}
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except Exception as e:
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error_msg = f"工作流执行失败: {e}"
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# 如果是JSON-RPC模式，发送错误响应
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if request_id is not None:
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handler = EnhancedJSONRPCResponse(request_id)
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handler.error(-32603, error_msg, {"exception": str(e)})
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return {
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"jsonrpc_mode": True,
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"request_id": request_id,
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"error_sent": True,
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"error": error_msg
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}
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else:
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# 非JSON-RPC模式：抛出异常
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logger.error(f"❌ {error_msg}")
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raise
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242
python_core/scene_detection/workflows/workflow_nodes.py
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242
python_core/scene_detection/workflows/workflow_nodes.py
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@@ -0,0 +1,242 @@
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#!/usr/bin/env python3
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"""
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Workflow Nodes
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工作流节点定义
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"""
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from pathlib import Path
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from typing import Dict, Any
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from dataclasses import asdict
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from python_core.utils.logger import logger
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from python_core.utils.jsonrpc_enhanced import ProgressLevel
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from ..types import SceneDetectionWorkflowState, DetectorType
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from ..services import SceneDetectorService, VideoInfoService, AIAnalysisService
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class WorkflowNodes:
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"""工作流节点集合"""
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def __init__(self):
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self.detector_service = SceneDetectorService()
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self.video_info_service = VideoInfoService()
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self.ai_analysis_service = AIAnalysisService()
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def validate_input(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""验证输入参数"""
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state.send_progress("validate", "🔍 验证输入参数...", ProgressLevel.INFO)
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video_path = Path(state.video_path)
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errors = []
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# 验证文件存在
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if not video_path.exists():
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errors.append(f"视频文件不存在: {video_path}")
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state.send_progress("validate", f"❌ 文件不存在: {video_path}", ProgressLevel.ERROR)
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# 验证文件格式
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if video_path.suffix.lower() not in self.detector_service.supported_formats:
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errors.append(f"不支持的文件格式: {video_path.suffix}")
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state.send_progress("validate", f"❌ 不支持的格式: {video_path.suffix}", ProgressLevel.ERROR)
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# 验证参数范围
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if not (0 <= state.threshold <= 100):
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errors.append(f"阈值超出范围 (0-100): {state.threshold}")
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state.send_progress("validate", f"❌ 阈值超出范围: {state.threshold}", ProgressLevel.ERROR)
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if state.min_scene_length < 0:
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errors.append(f"最小场景长度不能为负数: {state.min_scene_length}")
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state.send_progress("validate", f"❌ 最小场景长度无效: {state.min_scene_length}", ProgressLevel.ERROR)
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if not errors:
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state.send_progress("validate", "✅ 输入参数验证通过", ProgressLevel.SUCCESS)
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return {
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"current_stage": "validated" if not errors else "error",
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"progress": 1,
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"errors": errors
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}
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def extract_video_info(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""提取视频信息"""
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state.send_progress("extract_info", "📊 提取视频信息...", ProgressLevel.INFO)
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try:
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video_info = self.video_info_service.extract_video_info(Path(state.video_path))
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state.send_progress("extract_info",
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f"📹 视频信息: {video_info['resolution']}, {video_info['fps']:.2f}fps, {video_info['duration']:.2f}s",
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ProgressLevel.SUCCESS,
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{"video_info": video_info}
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)
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return {
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"current_stage": "info_extracted",
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"progress": 2,
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"video_info": video_info
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}
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except Exception as e:
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error_msg = f"提取视频信息失败: {e}"
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state.send_progress("extract_info", f"❌ {error_msg}", ProgressLevel.ERROR)
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return {
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"current_stage": "error",
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"errors": state.errors + [error_msg]
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}
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def detect_scenes(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""执行场景检测"""
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state.send_progress("detect", "🎯 执行场景检测...", ProgressLevel.INFO)
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try:
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result = self.detector_service.detect_scenes(
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Path(state.video_path),
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DetectorType(state.detector_type),
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state.threshold,
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state.min_scene_length
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)
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state.send_progress("detect",
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f"✅ 检测完成: {result.total_scenes} 个场景，耗时 {result.detection_time:.2f}秒",
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ProgressLevel.SUCCESS,
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{
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"total_scenes": result.total_scenes,
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"detection_time": result.detection_time,
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"total_duration": result.total_duration
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}
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)
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return {
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"current_stage": "scenes_detected",
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"progress": 3,
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"detection_result": result,
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"processed_scenes": result.scenes
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}
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except Exception as e:
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error_msg = f"场景检测失败: {e}"
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state.send_progress("detect", f"❌ {error_msg}", ProgressLevel.ERROR)
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return {
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"current_stage": "error",
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"errors": state.errors + [error_msg]
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}
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def analyze_with_ai(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""AI分析场景结果"""
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if not self.ai_analysis_service.ai_enabled or not state.enable_ai_analysis:
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state.send_progress("analyze", "⚠️ AI分析已禁用，跳过此步骤", ProgressLevel.WARNING)
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return {
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"current_stage": "analysis_skipped",
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"progress": 4,
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"ai_analysis": "AI分析已禁用"
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}
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state.send_progress("analyze", "🧠 AI分析场景结果...", ProgressLevel.INFO)
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try:
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state.send_progress("analyze", "🤖 正在调用AI分析服务...", ProgressLevel.INFO)
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analysis = self.ai_analysis_service.analyze_detection_result(
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state.detection_result,
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state.video_info
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)
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state.send_progress("analyze", "✅ AI分析完成", ProgressLevel.SUCCESS,
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{"analysis_length": len(analysis)})
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return {
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"current_stage": "ai_analyzed",
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"progress": 4,
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"ai_analysis": analysis
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}
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except Exception as e:
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error_msg = f"AI分析失败: {e}"
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state.send_progress("analyze", f"⚠️ {error_msg}", ProgressLevel.WARNING)
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return {
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"current_stage": "analysis_failed",
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"progress": 4,
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"ai_analysis": error_msg
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}
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def finalize_results(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""整理最终结果"""
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state.send_progress("finalize", "📋 整理最终结果...", ProgressLevel.INFO)
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# 保存结果（如果指定了输出路径）
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if state.output_path and state.detection_result:
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try:
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from ..utils import ResultSaver
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from ..types import OutputFormat
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output_path = Path(state.output_path)
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output_format = OutputFormat(state.output_format)
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saver = ResultSaver()
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saver.save_results(state.detection_result, output_path, output_format)
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state.send_progress("finalize", f"💾 结果已保存到: {output_path}", ProgressLevel.SUCCESS)
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except Exception as e:
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state.send_progress("finalize", f"⚠️ 保存结果失败: {e}", ProgressLevel.WARNING)
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# 准备最终结果
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final_result = {
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"success": True,
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"workflow_state": "completed",
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"detection_result": None,
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"ai_analysis": state.ai_analysis,
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"video_info": state.video_info,
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"errors": state.errors or []
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}
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# 序列化检测结果
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if state.detection_result:
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final_result["detection_result"] = {
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"success": state.detection_result.success,
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"filename": state.detection_result.filename,
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"detector_type": state.detection_result.detector_type,
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"threshold": state.detection_result.threshold,
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"total_scenes": state.detection_result.total_scenes,
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"total_duration": state.detection_result.total_duration,
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"detection_time": state.detection_result.detection_time,
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"scenes": [asdict(scene) for scene in state.detection_result.scenes],
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"error": state.detection_result.error
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}
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state.send_progress("finalize", "🎉 工作流执行完成", ProgressLevel.SUCCESS, {
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"total_scenes": state.detection_result.total_scenes if state.detection_result else 0,
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"workflow_stage": "completed"
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})
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# 发送最终结果到JSON-RPC客户端
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state.send_final_result(final_result)
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return {
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"current_stage": "completed",
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"progress": 5,
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"final_result": final_result
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}
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def handle_error(self, state: SceneDetectionWorkflowState) -> Dict[str, Any]:
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"""处理错误"""
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error_msg = "; ".join(state.errors) if state.errors else "未知错误"
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# 发送错误进度
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state.send_progress("error", f"❌ 工作流错误: {error_msg}", ProgressLevel.ERROR)
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# 准备错误结果
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error_result = {
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"success": False,
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"workflow_state": "failed",
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"error": error_msg,
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"errors": state.errors or [],
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"detection_result": None,
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"ai_analysis": None,
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"video_info": state.video_info
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}
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# 发送错误结果到JSON-RPC客户端
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state.send_error_result(-32603, f"工作流执行失败: {error_msg}", error_result)
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return {
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"current_stage": "failed",
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"error_result": error_result
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}
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