fix

python_core/cli/commands/scene.py

@@ -0,0 +1,344 @@
+#!/usr/bin/env python3
+"""
+场景检测命令模块
+"""
+
+from pathlib import Path
+from typing import Optional
+import typer
+
+from python_core.cli.const import progress_reporter, console
+from json import dumps
+# 创建场景检测命令组
+scene_app = typer.Typer(help="🎯 场景检测工具")
+
+@scene_app.command()
+def detect(
+    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)")
+):
+    """🎯 检测单个视频的场景"""
+    try:
+        from python_core.cli.scene_detect import detector as scene_detector, DetectorType, OutputFormat
+        
+        # 验证参数
+        try:
+            detector_type = DetectorType(detector)
+        except ValueError:
+            progress_reporter.error(f"❌ 无效的检测器类型: {detector}")
+            progress_reporter.info("💡 可用类型: content, threshold, adaptive")
+            raise typer.Exit(1)
+        
+        try:
+            output_format = OutputFormat(format)
+        except ValueError:
+            progress_reporter.error(f"❌ 无效的输出格式: {format}")
+            progress_reporter.info("💡 可用格式: json, csv, txt")
+            raise typer.Exit(1)
+        
+        # 执行检测
+        result = scene_detector.detect_scenes(
+            video_path, detector_type, threshold, min_scene_length
+        )
+        
+        if not result.success:
+            progress_reporter.error(f"❌ 检测失败: {result.error}")
+            raise typer.Exit(1)
+        
+        # 显示结果摘要
+        console.print(f"📊 检测结果摘要:")
+        console.print(f"   文件: {result.filename}")
+        console.print(f"   检测器: {result.detector_type}")
+        console.print(f"   阈值: {result.threshold}")
+        console.print(f"   场景数: {result.total_scenes}")
+        console.print(f"   总时长: {result.total_duration:.2f}秒")
+        console.print(f"   检测时间: {result.detection_time:.2f}秒")
+        
+        # 显示场景详情
+        if result.scenes:
+            console.print(f"\n🎬 场景列表:")
+            for scene in result.scenes[:10]:  # 只显示前10个场景
+                console.print(f"   场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s ({scene.duration:.2f}s)")
+            
+            if len(result.scenes) > 10:
+                console.print(f"   ... 还有 {len(result.scenes) - 10} 个场景")
+        
+        # 保存结果
+        if output:
+            scene_detector.save_results(result, output, output_format)
+            progress_reporter.success(f"📄 结果已保存到: {output}")
+        
+        return result
+        
+    except Exception as e:
+        progress_reporter.error(f"❌ 命令执行失败: {e}")
+        raise typer.Exit(1)
+
+@scene_app.command()
+def batch_detect(
+    input_directory: Path = typer.Argument(..., help="📁 输入目录路径", 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)"),
+    recursive: bool = typer.Option(False, "--recursive", "-r", help="🔄 递归扫描子目录"),
+    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)")
+):
+    """📦 批量检测目录中的所有视频"""
+    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)
+        
+        # 执行批量检测
+        results = scene_detector.batch_detect(
+            input_directory, detector_type, threshold, recursive
+        )
+        
+        if not results:
+            progress_reporter.warning("⚠️ 没有检测到任何视频文件")
+            return
+        
+        # 统计结果
+        successful = len([r for r in results if r.success])
+        failed = len(results) - successful
+        total_scenes = sum(r.total_scenes for r in results if r.success)
+        total_duration = sum(r.total_duration for r in results if r.success)
+
+        console.print(f"📊 批量检测结果:")
+        console.print(f"   总文件数: {len(results)}")
+        console.print(f"   成功: {successful}")
+        console.print(f"   失败: {failed}")
+        console.print(f"   总场景数: {total_scenes}")
+        console.print(f"   总时长: {total_duration:.2f}秒")
+
+        # 显示详细的场景信息
+        console.print(f"\n🎬 详细场景信息:")
+        for result in results:
+            if result.success and result.scenes:
+                console.print(f"\n📹 {result.filename} ({result.total_scenes} 个场景):")
+                for scene in result.scenes:
+                    console.print(f"   场景 {scene.index}: {scene.start_time:.2f}s - {scene.end_time:.2f}s (时长: {scene.duration:.2f}s)")
+            elif result.success:
+                console.print(f"\n📹 {result.filename}: 无场景数据")
+
+        # 显示失败的文件
+        failed_files = [r for r in results if not r.success]
+        if failed_files:
+            console.print(f"\n❌ 失败的文件:")
+            for result in failed_files[:5]:  # 只显示前5个失败文件
+                console.print(f"   {result.filename}: {result.error}")
+
+            if len(failed_files) > 5:
+                console.print(f"   ... 还有 {len(failed_files) - 5} 个失败文件")
+        
+        # 保存结果
+        if output:
+            scene_detector.save_results(results, output, output_format)
+            progress_reporter.success(f"📄 结果已保存到: {output}")
+        
+        return results
+        
+    except Exception as e:
+        progress_reporter.error(f"❌ 批量检测失败: {e}")
+        raise typer.Exit(1)
+
+@scene_app.command()
+def compare(
+    video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True),
+    thresholds: str = typer.Option("20,30,40", help="🎚️ 测试阈值列表（逗号分隔）"),
+    output: Optional[Path] = typer.Option(None, "--output", "-o", help="📄 输出文件路径")
+):
+    """🔬 比较不同检测器的效果"""
+    try:
+        from python_core.cli.scene_detect import detector as scene_detector
+        
+        # 解析阈值列表
+        try:
+            threshold_list = [float(t.strip()) for t in thresholds.split(",")]
+        except ValueError:
+            progress_reporter.error("❌ 无效的阈值格式，请使用逗号分隔的数字")
+            raise typer.Exit(1)
+        
+        # 执行比较
+        result = scene_detector.compare_detectors(video_path, threshold_list)
+        
+        # 显示分析结果
+        analysis = result["analysis"]
+        console.print(f"🔬 检测器比较结果:")
+        console.print(f"   视频: {Path(result['video_path']).name}")
+        console.print(f"   总测试数: {result['total_tests']}")
+        console.print(f"   成功测试数: {analysis['total_successful']}")
+        console.print(f"   推荐检测器: {analysis['best_detector']}")
+        console.print(f"   建议: {analysis['recommendation']}")
+        
+        # 显示详细分析
+        console.print(f"\n📊 各检测器表现:")
+        for detector_name, stats in analysis["detector_analysis"].items():
+            console.print(f"   🔧 {detector_name}:")
+            console.print(f"      平均场景数: {stats['average_scenes']:.1f}")
+            console.print(f"      平均检测时间: {stats['average_detection_time']:.2f}秒")
+            console.print(f"      测试次数: {stats['test_count']}")
+        
+        # 显示详细测试结果
+        console.print(f"\n🧪 详细测试结果:")
+        for test_result in result["results"]:
+            if test_result["success"]:
+                console.print(f"   {test_result['detector']} (阈值: {test_result['threshold']}): "
+                            f"{test_result['scenes']} 场景, {test_result['detection_time']:.2f}s")
+            else:
+                console.print(f"   {test_result['detector']} (阈值: {test_result['threshold']}): "
+                            f"❌ {test_result['error']}")
+        
+        # 保存结果
+        if output:
+            scene_detector.save_results(result, output)
+            progress_reporter.success(f"📄 结果已保存到: {output}")
+        
+        return result
+        
+    except Exception as e:
+        progress_reporter.error(f"❌ 比较测试失败: {e}")
+        raise typer.Exit(1)
+
+@scene_app.command()
+def split(
+    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)"),
+    output_dir: Optional[Path] = typer.Option(None, "--output-dir", "-d", help="📁 输出目录"),
+    filename_template: str = typer.Option("scene_{:03d}.mp4", help="📝 文件名模板")
+):
+    """✂️ 根据场景检测结果分割视频"""
+    try:
+        from python_core.cli.scene_detect import detector as scene_detector, DetectorType
+        from scenedetect.video_splitter import split_video_ffmpeg
+        
+        # 验证参数
+        try:
+            detector_type = DetectorType(detector)
+        except ValueError:
+            progress_reporter.error(f"❌ 无效的检测器类型: {detector}")
+            raise typer.Exit(1)
+        
+        # 设置输出目录
+        if output_dir is None:
+            output_dir = video_path.parent / f"{video_path.stem}_scenes"
+        
+        output_dir.mkdir(parents=True, exist_ok=True)
+        
+        # 先检测场景
+        progress_reporter.info("🎯 正在检测场景...")
+        result = scene_detector.detect_scenes(video_path, detector_type, threshold)
+        
+        if not result.success:
+            progress_reporter.error(f"❌ 场景检测失败: {result.error}")
+            raise typer.Exit(1)
+        
+        if not result.scenes:
+            progress_reporter.warning("⚠️ 未检测到任何场景")
+            return
+        
+        # 构建场景列表（PySceneDetect格式）
+        from scenedetect import FrameTimecode
+        scene_list = []
+        
+        # 假设视频帧率（实际应该从视频中获取）
+        fps = 25.0  # 默认帧率，实际使用时应该从视频文件中获取
+        
+        for scene in result.scenes:
+            start_tc = FrameTimecode(timecode=scene.start_time, fps=fps)
+            end_tc = FrameTimecode(timecode=scene.end_time, fps=fps)
+            scene_list.append((start_tc, end_tc))
+        
+        # 分割视频
+        progress_reporter.info(f"✂️ 正在分割视频到 {len(scene_list)} 个场景...")
+        
+        try:
+            split_video_ffmpeg(
+                input_video_path=str(video_path),
+                scene_list=scene_list,
+                output_file_template=str(output_dir / filename_template),
+                video_name=video_path.stem,
+                arg_override=None,
+                hide_progress=False
+            )
+            
+            progress_reporter.success(f"✅ 视频分割完成，输出到: {output_dir}")
+            console.print(f"📁 输出目录: {output_dir}")
+            console.print(f"🎬 场景数量: {len(scene_list)}")
+            
+            # 列出生成的文件
+            output_files = list(output_dir.glob("*.mp4"))
+            if output_files:
+                console.print(f"\n📄 生成的文件:")
+                for file_path in sorted(output_files)[:10]:
+                    console.print(f"   {file_path.name}")
+                
+                if len(output_files) > 10:
+                    console.print(f"   ... 还有 {len(output_files) - 10} 个文件")
+            
+        except Exception as e:
+            progress_reporter.error(f"❌ 视频分割失败: {e}")
+            raise typer.Exit(1)
+        
+    except Exception as e:
+        progress_reporter.error(f"❌ 分割命令执行失败: {e}")
+        raise typer.Exit(1)
+
+@scene_app.command()
+def info(
+    video_path: Path = typer.Argument(..., help="📹 视频文件路径", exists=True)
+):
+    """📋 显示视频基本信息"""
+    try:
+        import cv2
+
+        progress_reporter.info(f"📋 获取视频信息: {video_path.name}")
+
+        # 使用OpenCV获取视频信息
+        cap = cv2.VideoCapture(str(video_path))
+
+        if not cap.isOpened():
+            raise Exception("无法打开视频文件")
+
+        # 获取视频信息
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration = frame_count / fps if fps > 0 else 0
+        resolution = (width, height)
+
+        cap.release()
+        
+        # 显示信息
+        console.print(f"📹 视频信息:")
+        console.print(f"   文件名: {video_path.name}")
+        console.print(f"   文件大小: {video_path.stat().st_size / (1024*1024):.2f} MB")
+        console.print(f"   分辨率: {resolution[0]}x{resolution[1]}")
+        console.print(f"   帧率: {fps:.2f} fps")
+        console.print(f"   总帧数: {frame_count}")
+        console.print(f"   时长: {duration:.2f}秒 ({duration//60:.0f}分{duration%60:.0f}秒)")
+        
+        progress_reporter.success(dumps({
+            "filename": video_path.name,
+            "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)