fix

python_core/cli/commands/scene_detect.py

@@ -13,10 +13,11 @@ from rich.console import Console
 from rich.table import Table
 
 from python_core.scene_detection import (
-    SceneDetector, 
-    DetectorType, 
+    SceneDetector,
+    DetectorType,
     OutputFormat
 )
+from python_core.scene_detection.single_scene_detector import SingleSceneDetector
 from python_core.utils.logger import logger
 
 scene_detect = typer.Typer(help="场景检测工具 - 重构版")
@@ -231,5 +232,212 @@ def _display_batch_results_table(tasks):
     console.print(table)
 
 
+@scene_detect.command("single")
+def single_detect_and_split(
+    video_path: str = typer.Argument(..., help="视频文件路径"),
+    output_dir: Optional[str] = typer.Option(None, "--output", "-o", help="输出目录"),
+    detector_type: str = typer.Option("content", "--detector", "-d", help="检测器类型 (content/threshold/adaptive)"),
+    threshold: float = typer.Option(30.0, "--threshold", "-t", help="检测阈值 (0-100)"),
+    min_scene_length: float = typer.Option(1.0, "--min-length", "-l", help="最小场景长度（秒）"),
+    output_format: str = typer.Option("json", "--format", "-f", help="输出格式 (json/csv/txt)"),
+    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"),
+    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)"),
+    max_duration: float = typer.Option(2.0, "--max-duration", "-m", help="最大视频时长限制（秒），超过将二次切分"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
+):
+    """单个视频文件的场景检测和切分"""
+
+    try:
+        console.print(f"🎬 [bold blue]单个视频场景检测和切分[/bold blue]")
+        console.print(f"📁 视频文件: {video_path}")
+
+        # 验证输入参数
+        try:
+            detector_type_enum = DetectorType(detector_type)
+            output_format_enum = OutputFormat(output_format)
+        except ValueError as e:
+            console.print(f"[red]❌ 参数错误: {e}[/red]")
+            raise typer.Exit(1)
+
+        # 创建检测器
+        detector = SingleSceneDetector()
+
+        # 验证视频文件
+        validation_result = detector.validate_video(video_path)
+        if not validation_result["valid"]:
+            console.print(f"[red]❌ 视频文件验证失败: {validation_result['error']}[/red]")
+            raise typer.Exit(1)
+
+        console.print(f"✅ 视频文件验证通过")
+        console.print(f"📊 文件大小: {validation_result['file_size']:,} 字节")
+
+        # 执行处理
+        result = detector.detect_and_split(
+            video_path=video_path,
+            output_dir=output_dir,
+            detector_type=detector_type_enum,
+            threshold=threshold,
+            min_scene_length=min_scene_length,
+            output_format=output_format_enum,
+            enable_ai_analysis=ai_analysis,
+            enable_video_splitting=video_splitting,
+            use_advanced_split=use_advanced_split,
+            split_quality=split_quality,
+            split_preset=split_preset,
+            max_video_duration=max_duration
+        )
+
+        # 显示结果
+        if result["success"]:
+            console.print(f"\n✅ [bold green]处理完成![/bold green]")
+            console.print(f"📊 处理统计:")
+            console.print(f"   视频文件: {Path(result['video_path']).name}")
+            console.print(f"   输出目录: {result['output_dir']}")
+            console.print(f"   处理时间: {result['processing_time']:.1f}s")
+            console.print(f"   场景数量: {result['total_scenes']}")
+            console.print(f"   切分片段: {result['total_segments']}")
+
+            # 显示详细结果表格
+            if result.get("split_results") and verbose:
+                table = Table(title="切分结果详情")
+                table.add_column("片段", style="cyan")
+                table.add_column("输出文件", style="green")
+                table.add_column("时长", style="yellow")
+                table.add_column("文件大小", style="blue")
+                table.add_column("状态", style="magenta")
+
+                for split_result in result["split_results"]:
+                    status = "✅ 成功" if split_result["success"] else f"❌ {split_result.get('error', '失败')}"
+                    file_size = f"{split_result['file_size']:,} B" if split_result['file_size'] > 0 else "0 B"
+                    duration = f"{split_result['duration']:.2f}s"
+                    filename = Path(split_result['output_path']).name
+
+                    table.add_row(
+                        str(split_result['scene_index'] + 1),
+                        filename,
+                        duration,
+                        file_size,
+                        status
+                    )
+
+                console.print(table)
+        else:
+            console.print(f"\n[red]❌ 处理失败: {result.get('error', '未知错误')}[/red]")
+            raise typer.Exit(1)
+
+    except Exception as e:
+        console.print(f"\n[red]❌ 单个视频处理失败: {str(e)}[/red]")
+        raise typer.Exit(1)
+
+
+@scene_detect.command("import")
+def import_to_project(
+    video_path: str = typer.Argument(..., help="视频文件路径"),
+    project_id: str = typer.Argument(..., help="项目ID"),
+    project_directory: str = typer.Argument(..., help="项目目录路径"),
+    tags: Optional[str] = typer.Option(None, "--tags", "-t", help="素材标签，用逗号分隔"),
+    detector_type: str = typer.Option("content", "--detector", "-d", help="检测器类型"),
+    threshold: float = typer.Option(30.0, "--threshold", help="检测阈值"),
+    min_scene_length: float = typer.Option(1.0, "--min-length", help="最小场景长度（秒）"),
+    split_quality: int = typer.Option(23, "--quality", "-q", help="切分质量"),
+    split_preset: str = typer.Option("fast", "--preset", help="编码预设"),
+    max_duration: float = typer.Option(2.0, "--max-duration", "-m", help="最大视频时长限制（秒）"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="详细输出")
+):
+    """导入单个视频到项目素材库"""
+
+    try:
+        console.print(f"📦 [bold blue]导入视频到项目素材库[/bold blue]")
+        console.print(f"📁 视频文件: {video_path}")
+        console.print(f"🎯 项目ID: {project_id}")
+        console.print(f"📂 项目目录: {project_directory}")
+
+        # 解析标签
+        material_tags = []
+        if tags:
+            material_tags = [tag.strip() for tag in tags.split(",") if tag.strip()]
+            console.print(f"🏷️ 素材标签: {', '.join(material_tags)}")
+
+        # 验证输入参数
+        try:
+            detector_type_enum = DetectorType(detector_type)
+        except ValueError as e:
+            console.print(f"[red]❌ 参数错误: {e}[/red]")
+            raise typer.Exit(1)
+
+        # 创建检测器
+        detector = SingleSceneDetector()
+
+        # 验证视频文件
+        validation_result = detector.validate_video(video_path)
+        if not validation_result["valid"]:
+            console.print(f"[red]❌ 视频文件验证失败: {validation_result['error']}[/red]")
+            raise typer.Exit(1)
+
+        # 验证项目目录
+        project_dir_path = Path(project_directory)
+        if not project_dir_path.exists():
+            console.print(f"[red]❌ 项目目录不存在: {project_directory}[/red]")
+            raise typer.Exit(1)
+
+        console.print(f"✅ 验证通过，开始导入...")
+
+        # 执行导入
+        result = detector.import_to_project(
+            video_path=video_path,
+            project_id=project_id,
+            project_directory=project_directory,
+            material_tags=material_tags,
+            detector_type=detector_type_enum,
+            threshold=threshold,
+            min_scene_length=min_scene_length,
+            split_quality=split_quality,
+            split_preset=split_preset,
+            max_video_duration=max_duration
+        )
+
+        # 显示结果
+        if result["success"]:
+            console.print(f"\n✅ [bold green]导入完成![/bold green]")
+            console.print(f"📊 导入统计:")
+            console.print(f"   视频文件: {Path(result['video_path']).name}")
+            console.print(f"   处理时间: {result['processing_time']:.1f}s")
+            console.print(f"   场景数量: {result['total_scenes']}")
+            console.print(f"   导入片段: {result['total_segments']}")
+            console.print(f"   项目目录: {result['output_dir']}")
+
+            # 显示详细结果
+            if result.get("split_results") and verbose:
+                table = Table(title="导入素材详情")
+                table.add_column("片段", style="cyan")
+                table.add_column("时长", style="yellow")
+                table.add_column("文件大小", style="blue")
+                table.add_column("状态", style="magenta")
+
+                for split_result in result["split_results"]:
+                    status = "✅ 已导入" if split_result["success"] else f"❌ {split_result.get('error', '失败')}"
+                    file_size = f"{split_result['file_size']:,} B" if split_result['file_size'] > 0 else "0 B"
+                    duration = f"{split_result['duration']:.2f}s"
+
+                    table.add_row(
+                        str(split_result['scene_index'] + 1),
+                        duration,
+                        file_size,
+                        status
+                    )
+
+                console.print(table)
+        else:
+            console.print(f"\n[red]❌ 导入失败: {result.get('error', '未知错误')}[/red]")
+            raise typer.Exit(1)
+
+    except Exception as e:
+        console.print(f"\n[red]❌ 项目导入失败: {str(e)}[/red]")
+        raise typer.Exit(1)
+
+
 if __name__ == "__main__":
     scene_detect()