235 lines
10 KiB
Python
235 lines
10 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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@scene_detect.command("batch")
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def batch_detect_and_split(
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input_dir: Path = typer.Argument(..., help="包含视频文件的输入目录"),
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output_dir: Path = typer.Argument(..., help="输出目录"),
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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_format: OutputFormat = typer.Option(OutputFormat.JSON, "--format", "-f", help="输出格式"),
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ai_analysis: bool = typer.Option(False, "--ai/--no-ai", help="启用/禁用AI分析"),
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video_splitting: bool = typer.Option(True, "--split/--no-split", help="启用/禁用视频切分"),
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max_concurrent: int = typer.Option(2, "--concurrent", "-c", help="最大并发数"),
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continue_on_error: bool = typer.Option(True, "--continue/--stop-on-error", help="遇到错误时继续/停止"),
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file_pattern: str = typer.Option("*.mp4", "--pattern", "-p", help="视频文件匹配模式"),
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use_advanced_split: bool = typer.Option(True, "--advanced/--traditional", help="使用高效批量切分/传统逐个切分"),
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split_quality: int = typer.Option(23, "--quality", "-q", help="切分质量 (CRF值, 18-28)"),
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split_preset: str = typer.Option("fast", "--preset", help="编码预设 (ultrafast/fast/medium/slow)"),
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max_duration: float = typer.Option(2.0, "--max-duration", "-d", help="最大视频时长限制（秒），超过将二次切分"),
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verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
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):
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"""批量场景检测和视频切分"""
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console.print(f"🔄 批量处理目录: [bold blue]{input_dir}[/bold blue]")
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console.print(f"📂 输出目录: [bold blue]{output_dir}[/bold blue]")
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try:
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# 检查输入目录
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if not input_dir.exists() or not input_dir.is_dir():
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console.print(f"❌ 输入目录不存在或不是目录: [bold red]{input_dir}[/bold red]")
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raise typer.Exit(1)
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# 查找视频文件
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video_extensions = ['*.mp4', '*.avi', '*.mov', '*.mkv', '*.wmv', '*.flv', '*.webm', '*.m4v']
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video_files = []
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if file_pattern in video_extensions:
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# 使用指定的模式
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video_files = list(input_dir.glob(file_pattern))
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else:
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# 使用自定义模式
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video_files = list(input_dir.glob(file_pattern))
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# 如果自定义模式没找到文件，尝试所有支持的格式
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if not video_files:
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for pattern in video_extensions:
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video_files.extend(input_dir.glob(pattern))
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if not video_files:
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console.print(f"❌ 在目录中未找到视频文件: [bold red]{input_dir}[/bold red]")
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console.print(f"💡 尝试的模式: {file_pattern}")
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raise typer.Exit(1)
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console.print(f"📹 找到 {len(video_files)} 个视频文件")
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# 创建检测器
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detector = SceneDetector()
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# 执行批量处理
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result = detector.batch_detect_and_split(
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video_paths=video_files,
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output_base_dir=output_dir,
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detector_type=detector_type,
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threshold=threshold,
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min_scene_length=min_scene_length,
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output_format=output_format,
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enable_ai_analysis=ai_analysis,
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enable_video_splitting=video_splitting,
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max_concurrent=max_concurrent,
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continue_on_error=continue_on_error,
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use_advanced_split=use_advanced_split,
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split_quality=split_quality,
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split_preset=split_preset,
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max_video_duration=max_duration
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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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summary = result.get("batch_results", {})
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console.print(f"\n✅ 批量处理完成!")
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console.print(f"📊 处理统计:")
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console.print(f" 总视频数: {summary.get('total_videos', 0)}")
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console.print(f" 成功处理: {summary.get('completed_videos', 0)}")
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console.print(f" 处理失败: {summary.get('failed_videos', 0)}")
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console.print(f" 成功率: {summary.get('success_rate', 0):.1f}%")
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if video_splitting:
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tasks_data = summary.get('tasks', [])
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if tasks_data:
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total_scenes = sum(task.get('total_scenes', 0) for task in tasks_data)
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total_splits = sum(task.get('split_count', 0) for task in tasks_data)
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console.print(f" 总场景数: {total_scenes}")
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console.print(f" 切分片段: {total_splits}")
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else:
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console.print(" ⚠️ 无任务数据")
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# 显示详细结果
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if verbose:
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tasks = summary.get('tasks', [])
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if tasks:
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_display_batch_results_table(tasks)
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else:
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console.print(" ⚠️ 无详细任务数据可显示")
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else:
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console.print(f"❌ 批量处理失败")
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errors = result.get("errors", [])
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if errors:
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for error in errors:
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console.print(f" • {error}")
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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_batch_results_table(tasks):
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"""显示批量处理结果表格"""
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table = Table(title="批量处理结果")
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table.add_column("视频文件", style="cyan")
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table.add_column("状态", style="green")
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table.add_column("场景数", style="yellow")
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table.add_column("切分数", style="blue")
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table.add_column("处理时间", style="magenta")
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table.add_column("错误", style="red")
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for task in tasks:
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video_name = Path(task["video_path"]).name
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status = "✅ 成功" if task["status"] == "completed" else "❌ 失败"
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scenes = str(task.get("total_scenes", 0))
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splits = str(task.get("split_count", 0))
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proc_time = f"{task.get('processing_time', 0):.1f}s"
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error_text = task.get("error") or ""
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error = error_text[:50] + "..." if len(error_text) > 50 else error_text
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table.add_row(video_name, status, scenes, splits, proc_time, error)
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console.print(table)
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if __name__ == "__main__":
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scene_detect() |