230 lines
7.8 KiB
Python
230 lines
7.8 KiB
Python
#!/usr/bin/env python3
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"""
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测试场景检测功能的脚本
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比较PySceneDetect和OpenCV的检测效果
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"""
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import sys
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import os
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import time
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from pathlib import Path
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# 添加项目根目录到Python路径
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from python_core.services.media_manager import MediaManager
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def find_test_videos():
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"""查找测试视频文件"""
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print("🔍 查找测试视频文件...")
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# 可能的视频文件位置
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search_paths = [
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Path("/root/.mixvideo/temp/video_segments"),
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Path("/root/.mixvideo/temp/original_videos"),
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project_root / "assets" / "templates",
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]
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video_files = []
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for search_path in search_paths:
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if search_path.exists():
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print(f"📁 搜索目录: {search_path}")
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if search_path.is_file() and search_path.suffix.lower() in ['.mp4', '.avi', '.mov', '.mkv']:
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video_files.append(str(search_path))
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elif search_path.is_dir():
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# 递归查找视频文件
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for video_file in search_path.rglob("*"):
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if video_file.is_file() and video_file.suffix.lower() in ['.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv']:
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video_files.append(str(video_file))
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if len(video_files) >= 5: # 限制数量
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break
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print(f"📊 找到 {len(video_files)} 个视频文件")
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for i, video in enumerate(video_files[:5]):
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file_size = Path(video).stat().st_size / 1024 / 1024
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print(f" {i+1}. {Path(video).name} ({file_size:.1f} MB)")
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return video_files[:3] # 只返回前3个用于测试
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def test_scene_detection_methods(video_path: str):
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"""测试不同的场景检测方法"""
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print(f"\n🎬 测试视频: {Path(video_path).name}")
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print("-" * 50)
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media_manager = MediaManager()
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# 获取视频基本信息
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try:
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video_info = media_manager._get_video_info(video_path)
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print(f"📊 视频信息:")
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print(f" 时长: {video_info['duration']:.2f} 秒")
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print(f" 分辨率: {video_info['width']}x{video_info['height']}")
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print(f" 帧率: {video_info['fps']:.2f} fps")
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print(f" 文件大小: {video_info['file_size']/1024/1024:.1f} MB")
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except Exception as e:
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print(f"❌ 获取视频信息失败: {e}")
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return
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# 测试不同的阈值
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thresholds = [20.0, 30.0, 40.0]
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for threshold in thresholds:
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print(f"\n🔧 测试阈值: {threshold}")
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# 测试PySceneDetect方法
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try:
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start_time = time.time()
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scene_changes = media_manager._detect_scene_changes(video_path, threshold)
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pyscene_time = time.time() - start_time
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print(f"✅ PySceneDetect: {len(scene_changes)-1} 个场景变化 (耗时: {pyscene_time:.2f}s)")
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if scene_changes:
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print(f" 时间点: {[f'{t:.2f}s' for t in scene_changes[:10]]}") # 只显示前10个
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except Exception as e:
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print(f"❌ PySceneDetect失败: {e}")
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# 测试OpenCV方法
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try:
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start_time = time.time()
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opencv_changes = media_manager._detect_scene_changes_opencv(video_path, threshold)
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opencv_time = time.time() - start_time
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print(f"✅ OpenCV: {len(opencv_changes)-1} 个场景变化 (耗时: {opencv_time:.2f}s)")
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if opencv_changes:
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print(f" 时间点: {[f'{t:.2f}s' for t in opencv_changes[:10]]}") # 只显示前10个
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except Exception as e:
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print(f"❌ OpenCV失败: {e}")
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def test_scene_detection_accuracy():
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"""测试场景检测的准确性"""
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print("\n🎯 场景检测准确性测试")
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print("=" * 50)
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video_files = find_test_videos()
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if not video_files:
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print("❌ 没有找到测试视频文件")
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print("\n💡 建议:")
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print("1. 先导入一些视频素材")
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print("2. 或者将测试视频文件放到以下目录:")
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print(" - /root/.mixvideo/temp/video_segments/")
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print(" - /root/.mixvideo/temp/original_videos/")
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return
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for video_path in video_files:
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try:
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test_scene_detection_methods(video_path)
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except Exception as e:
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print(f"❌ 测试视频 {video_path} 失败: {e}")
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continue
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def benchmark_performance():
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"""性能基准测试"""
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print("\n⚡ 性能基准测试")
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print("=" * 50)
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video_files = find_test_videos()
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if not video_files:
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print("❌ 没有找到测试视频文件")
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return
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media_manager = MediaManager()
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# 选择一个中等大小的视频进行测试
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test_video = None
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for video in video_files:
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try:
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size = Path(video).stat().st_size / 1024 / 1024 # MB
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if 5 < size < 50: # 5-50MB的视频
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test_video = video
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break
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except:
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continue
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if not test_video:
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test_video = video_files[0] # 使用第一个视频
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print(f"🎬 性能测试视频: {Path(test_video).name}")
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methods = [
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("PySceneDetect", media_manager._detect_scene_changes),
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("OpenCV", media_manager._detect_scene_changes_opencv)
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]
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for method_name, method_func in methods:
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try:
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print(f"\n🔧 测试 {method_name}...")
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# 多次运行取平均值
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times = []
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results = []
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for i in range(3):
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start_time = time.time()
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scene_changes = method_func(test_video, 30.0)
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end_time = time.time()
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times.append(end_time - start_time)
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results.append(len(scene_changes) - 1)
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print(f" 运行 {i+1}: {end_time - start_time:.2f}s, {len(scene_changes)-1} 个场景")
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avg_time = sum(times) / len(times)
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avg_scenes = sum(results) / len(results)
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print(f"✅ {method_name} 平均性能:")
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print(f" 平均耗时: {avg_time:.2f}s")
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print(f" 平均场景数: {avg_scenes:.1f}")
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except Exception as e:
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print(f"❌ {method_name} 性能测试失败: {e}")
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def show_recommendations():
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"""显示使用建议"""
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print("\n💡 使用建议")
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print("=" * 50)
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print("🎯 选择合适的阈值:")
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print("- 阈值 10-20: 高敏感度，适合检测细微的场景变化")
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print("- 阈值 25-35: 中等敏感度，适合大多数情况 (推荐)")
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print("- 阈值 40-50: 低敏感度，只检测明显的场景变化")
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print("\n⚡ 性能考虑:")
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print("- PySceneDetect: 更准确，但处理时间较长")
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print("- OpenCV: 处理速度快，但准确性较低")
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print("- 建议: 对重要视频使用PySceneDetect，批量处理时使用OpenCV")
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print("\n🔧 优化建议:")
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print("- 对于长视频，可以先用低敏感度快速分割")
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print("- 然后对重要片段使用高敏感度精细分割")
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print("- 定期检查分割结果，调整阈值参数")
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def main():
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"""主函数"""
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print("🎬 场景检测功能测试")
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print("=" * 50)
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try:
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# 1. 测试场景检测准确性
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test_scene_detection_accuracy()
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# 2. 性能基准测试
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benchmark_performance()
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# 3. 显示使用建议
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show_recommendations()
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print("\n✅ 测试完成！")
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except Exception as e:
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print(f"❌ 测试过程中出现错误: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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