99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""
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Scene Detection CLI - Refactored
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场景检测命令行工具 - 重构版
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使用重构后的场景检测模块，代码更简洁、模块化更好。
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"""
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import typer
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from pathlib import Path
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from typing import Optional, List
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from rich.console import Console
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from rich.table import Table
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from python_core.scene_detection import (
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SceneDetector,
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DetectorType,
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OutputFormat
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)
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from python_core.utils.logger import logger
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scene_detect = typer.Typer(help="场景检测工具 - 重构版")
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console = Console()
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@scene_detect.command("detect")
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def detect(
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video_path: Path = typer.Argument(..., help="视频文件路径"),
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detector_type: DetectorType = typer.Option(DetectorType.CONTENT, "--detector", "-d", help="检测器类型"),
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threshold: float = typer.Option(30.0, "--threshold", "-t", help="检测阈值"),
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min_scene_length: float = typer.Option(1.0, "--min-length", "-m", 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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output_format: OutputFormat = typer.Option(OutputFormat.JSON, "--format", "-f", help="输出格式"),
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ai_analysis: bool = typer.Option(True, "--ai/--no-ai", help="启用/禁用AI分析"),
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verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
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):
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"""使用LangGraph工作流进行场景检测"""
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console.print(f"🔄 使用工作流检测视频: [bold blue]{video_path}[/bold blue]")
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try:
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# 创建检测器
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detector = SceneDetector()
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# 执行工作流检测
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result = detector.detect_with_workflow(
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video_path, detector_type, threshold, min_scene_length,
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output, output_format, ai_analysis
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)
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# 显示结果
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if result.get("workflow_state") == "completed":
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detection_result = result.get("detection_result")
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if detection_result and detection_result.success:
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console.print(f"✅ 工作流完成: [bold green]{detection_result.total_scenes}[/bold green] 个场景")
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console.print(f"📊 检测时间: {detection_result.detection_time:.2f}秒")
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# 显示AI分析结果
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ai_analysis_result = result.get("ai_analysis")
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if ai_analysis_result and ai_analysis_result != "AI分析已禁用":
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console.print("\n🧠 AI分析结果:")
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console.print(ai_analysis_result[:500] + "..." if len(ai_analysis_result) > 500 else ai_analysis_result)
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# 显示场景列表
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if verbose:
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_display_scenes_table(detection_result.scenes)
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else:
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console.print(f"❌ 检测失败: [bold red]{detection_result.error if detection_result else '未知错误'}[/bold red]")
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raise typer.Exit(1)
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else:
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errors = result.get("errors", [])
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error_msg = "; ".join(errors) if errors else "工作流执行失败"
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console.print(f"❌ 工作流失败: [bold red]{error_msg}[/bold red]")
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raise typer.Exit(1)
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except Exception as e:
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console.print(f"❌ 执行失败: [bold red]{str(e)}[/bold red]")
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raise typer.Exit(1)
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def _display_scenes_table(scenes):
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"""显示场景表格"""
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table = Table(title="检测到的场景")
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table.add_column("场景", style="cyan")
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table.add_column("开始时间", style="green")
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table.add_column("结束时间", style="green")
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table.add_column("时长", style="yellow")
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for scene in scenes:
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table.add_row(
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str(scene.index + 1),
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f"{scene.start_time:.2f}s",
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f"{scene.end_time:.2f}s",
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f"{scene.duration:.2f}s"
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)
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console.print(table)
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if __name__ == "__main__":
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scene_detect() |