fix

python_core/cli/commands/scene_detect.py

@@ -95,5 +95,139 @@ def _display_scenes_table(scenes):
     console.print(table)
 
 
+@scene_detect.command("batch")
+def batch_detect_and_split(
+    input_dir: Path = typer.Argument(..., help="包含视频文件的输入目录"),
+    output_dir: Path = typer.Argument(..., help="输出目录"),
+    detector_type: DetectorType = typer.Option(DetectorType.CONTENT, "--detector", "-d", help="检测器类型"),
+    threshold: float = typer.Option(30.0, "--threshold", "-t", help="检测阈值"),
+    min_scene_length: float = typer.Option(1.0, "--min-length", "-m", help="最小场景长度(秒)"),
+    output_format: OutputFormat = typer.Option(OutputFormat.JSON, "--format", "-f", help="输出格式"),
+    ai_analysis: bool = typer.Option(False, "--ai/--no-ai", help="启用/禁用AI分析"),
+    video_splitting: bool = typer.Option(True, "--split/--no-split", help="启用/禁用视频切分"),
+    max_concurrent: int = typer.Option(2, "--concurrent", "-c", help="最大并发数"),
+    continue_on_error: bool = typer.Option(True, "--continue/--stop-on-error", help="遇到错误时继续/停止"),
+    file_pattern: str = typer.Option("*.mp4", "--pattern", "-p", help="视频文件匹配模式"),
+    use_advanced_split: bool = typer.Option(True, "--advanced/--traditional", help="使用高效批量切分/传统逐个切分"),
+    split_quality: int = typer.Option(23, "--quality", "-q", help="切分质量 (CRF值, 18-28)"),
+    split_preset: str = typer.Option("fast", "--preset", help="编码预设 (ultrafast/fast/medium/slow)"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
+):
+    """批量场景检测和视频切分"""
+    console.print(f"🔄 批量处理目录: [bold blue]{input_dir}[/bold blue]")
+    console.print(f"📂 输出目录: [bold blue]{output_dir}[/bold blue]")
+
+    try:
+        # 检查输入目录
+        if not input_dir.exists() or not input_dir.is_dir():
+            console.print(f"❌ 输入目录不存在或不是目录: [bold red]{input_dir}[/bold red]")
+            raise typer.Exit(1)
+
+        # 查找视频文件
+        video_extensions = ['*.mp4', '*.avi', '*.mov', '*.mkv', '*.wmv', '*.flv', '*.webm', '*.m4v']
+
+        video_files = []
+        if file_pattern in video_extensions:
+            # 使用指定的模式
+            video_files = list(input_dir.glob(file_pattern))
+        else:
+            # 使用自定义模式
+            video_files = list(input_dir.glob(file_pattern))
+            # 如果自定义模式没找到文件，尝试所有支持的格式
+            if not video_files:
+                for pattern in video_extensions:
+                    video_files.extend(input_dir.glob(pattern))
+
+        if not video_files:
+            console.print(f"❌ 在目录中未找到视频文件: [bold red]{input_dir}[/bold red]")
+            console.print(f"💡 尝试的模式: {file_pattern}")
+            raise typer.Exit(1)
+
+        console.print(f"📹 找到 {len(video_files)} 个视频文件")
+
+        # 创建检测器
+        detector = SceneDetector()
+
+        # 执行批量处理
+        result = detector.batch_detect_and_split(
+            video_paths=video_files,
+            output_base_dir=output_dir,
+            detector_type=detector_type,
+            threshold=threshold,
+            min_scene_length=min_scene_length,
+            output_format=output_format,
+            enable_ai_analysis=ai_analysis,
+            enable_video_splitting=video_splitting,
+            max_concurrent=max_concurrent,
+            continue_on_error=continue_on_error,
+            use_advanced_split=use_advanced_split,
+            split_quality=split_quality,
+            split_preset=split_preset
+        )
+
+        # 显示结果
+        if result.get("workflow_state") == "completed":
+            summary = result.get("batch_results", {})
+            console.print(f"\n✅ 批量处理完成!")
+            console.print(f"📊 处理统计:")
+            console.print(f"   总视频数: {summary.get('total_videos', 0)}")
+            console.print(f"   成功处理: {summary.get('completed_videos', 0)}")
+            console.print(f"   处理失败: {summary.get('failed_videos', 0)}")
+            console.print(f"   成功率: {summary.get('success_rate', 0):.1f}%")
+
+            if video_splitting:
+                tasks_data = summary.get('tasks', [])
+                if tasks_data:
+                    total_scenes = sum(task.get('total_scenes', 0) for task in tasks_data)
+                    total_splits = sum(task.get('split_count', 0) for task in tasks_data)
+                    console.print(f"   总场景数: {total_scenes}")
+                    console.print(f"   切分片段: {total_splits}")
+                else:
+                    console.print("   ⚠️ 无任务数据")
+
+            # 显示详细结果
+            if verbose:
+                tasks = summary.get('tasks', [])
+                if tasks:
+                    _display_batch_results_table(tasks)
+                else:
+                    console.print("   ⚠️ 无详细任务数据可显示")
+        else:
+            console.print(f"❌ 批量处理失败")
+            errors = result.get("errors", [])
+            if errors:
+                for error in errors:
+                    console.print(f"   • {error}")
+            raise typer.Exit(1)
+
+    except Exception as e:
+        console.print(f"❌ 执行失败: [bold red]{str(e)}[/bold red]")
+        raise typer.Exit(1)
+
+
+def _display_batch_results_table(tasks):
+    """显示批量处理结果表格"""
+    table = Table(title="批量处理结果")
+    table.add_column("视频文件", style="cyan")
+    table.add_column("状态", style="green")
+    table.add_column("场景数", style="yellow")
+    table.add_column("切分数", style="blue")
+    table.add_column("处理时间", style="magenta")
+    table.add_column("错误", style="red")
+
+    for task in tasks:
+        video_name = Path(task["video_path"]).name
+        status = "✅ 成功" if task["status"] == "completed" else "❌ 失败"
+        scenes = str(task.get("total_scenes", 0))
+        splits = str(task.get("split_count", 0))
+        proc_time = f"{task.get('processing_time', 0):.1f}s"
+        error_text = task.get("error") or ""
+        error = error_text[:50] + "..." if len(error_text) > 50 else error_text
+
+        table.add_row(video_name, status, scenes, splits, proc_time, error)
+
+    console.print(table)
+
+
 if __name__ == "__main__":
     scene_detect()