#!/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)