550 lines
23 KiB
Python
550 lines
23 KiB
Python
#!/usr/bin/env python3
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"""
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场景检测命令模块
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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 typer
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from python_core.cli.const import progress_reporter, console
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from json import dumps
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# 创建场景检测命令组
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scene_app = typer.Typer(help="🎯 场景检测工具")
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@scene_app.command()
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def detect(
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video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True),
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detector: str = typer.Option("content", help="🔧 检测器类型 (content/threshold/adaptive)"),
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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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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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from python_core.cli.scene_detect import detector as scene_detector, DetectorType, OutputFormat
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# 验证参数
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try:
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detector_type = DetectorType(detector)
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except ValueError:
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progress_reporter.error(f"❌ 无效的检测器类型: {detector}")
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progress_reporter.info("💡 可用类型: content, threshold, adaptive")
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raise typer.Exit(1)
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try:
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output_format = OutputFormat(format)
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except ValueError:
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progress_reporter.error(f"❌ 无效的输出格式: {format}")
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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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# 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(
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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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except Exception as e:
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progress_reporter.error(f"❌ 命令执行失败: {e}")
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raise typer.Exit(1)
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@scene_app.command()
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def batch_detect(
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input_directory: Path = typer.Argument(..., help="📁 输入目录路径", exists=True),
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detector: str = typer.Option("content", help="🔧 检测器类型 (content/threshold/adaptive)"),
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threshold: float = typer.Option(30.0, help="🎚️ 检测阈值 (0-100)"),
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recursive: bool = typer.Option(False, "--recursive", "-r", help="🔄 递归扫描子目录"),
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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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):
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"""📦 批量检测目录中的所有视频"""
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try:
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from python_core.cli.scene_detect import detector as scene_detector, DetectorType, OutputFormat
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# 验证参数
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try:
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detector_type = DetectorType(detector)
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output_format = OutputFormat(format)
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except ValueError as e:
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progress_reporter.error(f"❌ 参数错误: {e}")
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raise typer.Exit(1)
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# 执行批量检测
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results = scene_detector.batch_detect(
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input_directory, detector_type, threshold, recursive
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)
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if not results:
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progress_reporter.warning("⚠️ 没有检测到任何视频文件")
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return
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# 统计结果
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successful = len([r for r in results if r.success])
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failed = len(results) - successful
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total_scenes = sum(r.total_scenes for r in results if r.success)
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total_duration = sum(r.total_duration for r in results if r.success)
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console.print(f"📊 批量检测结果:")
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console.print(f" 总文件数: {len(results)}")
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console.print(f" 成功: {successful}")
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console.print(f" 失败: {failed}")
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console.print(f" 总场景数: {total_scenes}")
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console.print(f" 总时长: {total_duration:.2f}秒")
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# 显示详细的场景信息
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console.print(f"\n🎬 详细场景信息:")
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for result in results:
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if result.success and result.scenes:
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console.print(f"\n📹 {result.filename} ({result.total_scenes} 个场景):")
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for scene in result.scenes:
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console.print(f" 场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s (时长: {scene.duration:.2f}s)")
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elif result.success:
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console.print(f"\n📹 {result.filename}: 无场景数据")
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# 显示失败的文件
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failed_files = [r for r in results if not r.success]
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if failed_files:
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console.print(f"\n❌ 失败的文件:")
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for result in failed_files[:5]: # 只显示前5个失败文件
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console.print(f" {result.filename}: {result.error}")
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if len(failed_files) > 5:
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console.print(f" ... 还有 {len(failed_files) - 5} 个失败文件")
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# 保存结果
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if output:
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scene_detector.save_results(results, output, output_format)
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progress_reporter.success(f"📄 结果已保存到: {output}")
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return results
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except Exception as e:
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progress_reporter.error(f"❌ 批量检测失败: {e}")
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raise typer.Exit(1)
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@scene_app.command()
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def compare(
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video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True),
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thresholds: str = typer.Option("20,30,40", help="🎚️ 测试阈值列表（逗号分隔）"),
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output: Optional[Path] = typer.Option(None, "--output", "-o", help="📄 输出文件路径")
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):
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"""🔬 比较不同检测器的效果"""
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try:
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from python_core.cli.scene_detect import detector as scene_detector
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# 解析阈值列表
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try:
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threshold_list = [float(t.strip()) for t in thresholds.split(",")]
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except ValueError:
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progress_reporter.error("❌ 无效的阈值格式，请使用逗号分隔的数字")
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raise typer.Exit(1)
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# 执行比较
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result = scene_detector.compare_detectors(video_path, threshold_list)
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# 显示分析结果
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analysis = result["analysis"]
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console.print(f"🔬 检测器比较结果:")
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console.print(f" 视频: {Path(result['video_path']).name}")
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console.print(f" 总测试数: {result['total_tests']}")
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console.print(f" 成功测试数: {analysis['total_successful']}")
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console.print(f" 推荐检测器: {analysis['best_detector']}")
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console.print(f" 建议: {analysis['recommendation']}")
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# 显示详细分析
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console.print(f"\n📊 各检测器表现:")
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for detector_name, stats in analysis["detector_analysis"].items():
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console.print(f" 🔧 {detector_name}:")
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console.print(f" 平均场景数: {stats['average_scenes']:.1f}")
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console.print(f" 平均检测时间: {stats['average_detection_time']:.2f}秒")
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console.print(f" 测试次数: {stats['test_count']}")
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# 显示详细测试结果
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console.print(f"\n🧪 详细测试结果:")
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for test_result in result["results"]:
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if test_result["success"]:
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console.print(f" {test_result['detector']} (阈值: {test_result['threshold']}): "
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f"{test_result['scenes']} 场景, {test_result['detection_time']:.2f}s")
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else:
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console.print(f" {test_result['detector']} (阈值: {test_result['threshold']}): "
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f"❌ {test_result['error']}")
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# 保存结果
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if output:
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scene_detector.save_results(result, output)
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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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except Exception as e:
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progress_reporter.error(f"❌ 比较测试失败: {e}")
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raise typer.Exit(1)
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@scene_app.command()
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def split(
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video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True),
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detector: str = typer.Option("content", help="🔧 检测器类型 (content/threshold/adaptive)"),
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threshold: float = typer.Option(30.0, help="🎚️ 检测阈值 (0-100)"),
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output_dir: Optional[Path] = typer.Option(None, "--output-dir", "-d", help="📁 输出目录"),
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filename_template: str = typer.Option("scene_{:03d}.mp4", help="📝 文件名模板")
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):
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"""✂️ 根据场景检测结果分割视频"""
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try:
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from python_core.cli.scene_detect import detector as scene_detector, DetectorType
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from scenedetect.video_splitter import split_video_ffmpeg
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# 验证参数
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try:
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detector_type = DetectorType(detector)
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except ValueError:
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progress_reporter.error(f"❌ 无效的检测器类型: {detector}")
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raise typer.Exit(1)
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# 设置输出目录
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if output_dir is None:
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output_dir = video_path.parent / f"{video_path.stem}_scenes"
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output_dir.mkdir(parents=True, exist_ok=True)
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# 先检测场景
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progress_reporter.info("🎯 正在检测场景...")
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result = scene_detector.detect_scenes(video_path, detector_type, threshold)
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if not result.success:
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progress_reporter.error(f"❌ 场景检测失败: {result.error}")
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raise typer.Exit(1)
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if not result.scenes:
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progress_reporter.warning("⚠️ 未检测到任何场景")
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return
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# 构建场景列表（PySceneDetect格式）
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from scenedetect import FrameTimecode
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scene_list = []
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# 假设视频帧率（实际应该从视频中获取）
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fps = 25.0 # 默认帧率，实际使用时应该从视频文件中获取
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for scene in result.scenes:
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start_tc = FrameTimecode(timecode=scene.start_time, fps=fps)
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end_tc = FrameTimecode(timecode=scene.end_time, fps=fps)
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scene_list.append((start_tc, end_tc))
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# 分割视频
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progress_reporter.info(f"✂️ 正在分割视频到 {len(scene_list)} 个场景...")
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try:
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split_video_ffmpeg(
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input_video_path=str(video_path),
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scene_list=scene_list,
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output_file_template=str(output_dir / filename_template),
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video_name=video_path.stem,
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arg_override=None,
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hide_progress=False
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)
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progress_reporter.success(f"✅ 视频分割完成，输出到: {output_dir}")
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console.print(f"📁 输出目录: {output_dir}")
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console.print(f"🎬 场景数量: {len(scene_list)}")
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# 列出生成的文件
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output_files = list(output_dir.glob("*.mp4"))
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if output_files:
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console.print(f"\n📄 生成的文件:")
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for file_path in sorted(output_files)[:10]:
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console.print(f" {file_path.name}")
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if len(output_files) > 10:
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console.print(f" ... 还有 {len(output_files) - 10} 个文件")
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except Exception as e:
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progress_reporter.error(f"❌ 视频分割失败: {e}")
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raise typer.Exit(1)
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except Exception as e:
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progress_reporter.error(f"❌ 分割命令执行失败: {e}")
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raise typer.Exit(1)
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@scene_app.command()
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def info(
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video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True)
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):
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"""📋 显示视频基本信息"""
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try:
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import cv2
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progress_reporter.info(f"📋 获取视频信息: {video_path.name}")
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# 使用OpenCV获取视频信息
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cap = cv2.VideoCapture(str(video_path))
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if not cap.isOpened():
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raise Exception("无法打开视频文件")
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# 获取视频信息
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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duration = frame_count / fps if fps > 0 else 0
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resolution = (width, height)
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cap.release()
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# 显示信息
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console.print(f"📹 视频信息:")
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console.print(f" 文件名: {video_path.name}")
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console.print(f" 文件大小: {video_path.stat().st_size / (1024*1024):.2f} MB")
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console.print(f" 分辨率: {resolution[0]}x{resolution[1]}")
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console.print(f" 帧率: {fps:.2f} fps")
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console.print(f" 总帧数: {frame_count}")
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console.print(f" 时长: {duration:.2f}秒 ({duration//60:.0f}分{duration%60:.0f}秒)")
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progress_reporter.success(dumps({
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"filename": video_path.name,
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"file_size_mb": video_path.stat().st_size / (1024*1024),
|
||
"resolution": resolution,
|
||
"fps": fps,
|
||
"frame_count": frame_count,
|
||
"duration": duration
|
||
}))
|
||
|
||
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)
|