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https://github.com/linyqh/NarratoAI.git
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6715c29057
在video_processor.py中，添加了对Windows N卡硬件加速的支持，优化了帧提取过程，改进了提取成功率的统计和错误处理。同时，在generate_script_docu.py中，增强了对硬件加速失败的处理逻辑，提供了详细的错误信息和解决建议，提升了用户体验。
285 lines
9.3 KiB
Python
285 lines
9.3 KiB
Python
"""
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FFmpeg 配置管理模块
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专门用于管理 FFmpeg 兼容性设置和优化参数
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"""
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import os
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import platform
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from typing import Dict, List, Optional
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from dataclasses import dataclass
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from loguru import logger
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@dataclass
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class FFmpegProfile:
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"""FFmpeg 配置文件"""
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name: str
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description: str
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hwaccel_enabled: bool
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hwaccel_type: Optional[str]
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encoder: str
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quality_preset: str
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pixel_format: str
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additional_args: List[str]
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compatibility_level: int # 1-5, 5为最高兼容性
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class FFmpegConfigManager:
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"""FFmpeg 配置管理器"""
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# 预定义的配置文件
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PROFILES = {
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# 高性能配置（适用于现代硬件）
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"high_performance": FFmpegProfile(
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name="high_performance",
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description="高性能配置（NVIDIA/AMD 独立显卡）",
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hwaccel_enabled=True,
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hwaccel_type="auto",
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encoder="auto",
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quality_preset="fast",
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pixel_format="yuv420p",
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additional_args=["-preset", "fast"],
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compatibility_level=2
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),
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# 兼容性配置（适用于有问题的硬件）
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"compatibility": FFmpegProfile(
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name="compatibility",
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description="兼容性配置（解决滤镜链问题）",
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hwaccel_enabled=False,
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hwaccel_type=None,
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encoder="libx264",
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quality_preset="medium",
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pixel_format="yuv420p",
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additional_args=["-preset", "medium", "-crf", "23"],
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compatibility_level=5
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),
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# Windows N 卡优化配置
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"windows_nvidia": FFmpegProfile(
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name="windows_nvidia",
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description="Windows NVIDIA 显卡优化配置",
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hwaccel_enabled=True,
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hwaccel_type="nvenc_pure", # 纯编码器，避免解码问题
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encoder="h264_nvenc",
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quality_preset="medium",
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pixel_format="yuv420p",
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additional_args=["-preset", "medium", "-cq", "23"],
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compatibility_level=3
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),
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# macOS 优化配置
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"macos_videotoolbox": FFmpegProfile(
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name="macos_videotoolbox",
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description="macOS VideoToolbox 优化配置",
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hwaccel_enabled=True,
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hwaccel_type="videotoolbox",
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encoder="h264_videotoolbox",
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quality_preset="medium",
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pixel_format="yuv420p",
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additional_args=["-q:v", "65"],
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compatibility_level=3
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),
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# 通用软件配置
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"universal_software": FFmpegProfile(
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name="universal_software",
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description="通用软件编码配置（最高兼容性）",
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hwaccel_enabled=False,
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hwaccel_type=None,
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encoder="libx264",
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quality_preset="medium",
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pixel_format="yuv420p",
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additional_args=["-preset", "medium", "-crf", "23"],
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compatibility_level=5
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)
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}
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@classmethod
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def get_recommended_profile(cls) -> str:
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"""
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根据系统环境推荐最佳配置文件
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Returns:
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str: 推荐的配置文件名称
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"""
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system = platform.system().lower()
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# 检测硬件加速可用性
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try:
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from app.utils import ffmpeg_utils
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hwaccel_info = ffmpeg_utils.get_ffmpeg_hwaccel_info()
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hwaccel_available = hwaccel_info.get("available", False)
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hwaccel_type = hwaccel_info.get("type", "software")
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gpu_vendor = hwaccel_info.get("gpu_vendor", "unknown")
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except Exception as e:
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logger.warning(f"无法检测硬件加速信息: {e}")
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hwaccel_available = False
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hwaccel_type = "software"
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gpu_vendor = "unknown"
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# 根据平台和硬件推荐配置
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if system == "windows":
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if hwaccel_available and gpu_vendor == "nvidia":
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return "windows_nvidia"
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elif hwaccel_available:
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return "high_performance"
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else:
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return "compatibility"
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elif system == "darwin":
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if hwaccel_available and hwaccel_type == "videotoolbox":
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return "macos_videotoolbox"
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else:
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return "universal_software"
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elif system == "linux":
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if hwaccel_available:
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return "high_performance"
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else:
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return "universal_software"
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else:
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return "universal_software"
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@classmethod
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def get_profile(cls, profile_name: str) -> FFmpegProfile:
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"""
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获取指定的配置文件
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Args:
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profile_name: 配置文件名称
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Returns:
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FFmpegProfile: 配置文件对象
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"""
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if profile_name not in cls.PROFILES:
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logger.warning(f"未知的配置文件: {profile_name}，使用默认配置")
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profile_name = "universal_software"
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return cls.PROFILES[profile_name]
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@classmethod
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def get_extraction_command(cls,
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input_path: str,
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output_path: str,
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timestamp: float,
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profile_name: Optional[str] = None) -> List[str]:
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"""
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根据配置文件生成关键帧提取命令
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Args:
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input_path: 输入视频路径
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output_path: 输出图片路径
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timestamp: 时间戳
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profile_name: 配置文件名称，None 表示自动选择
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Returns:
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List[str]: FFmpeg 命令列表
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"""
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if profile_name is None:
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profile_name = cls.get_recommended_profile()
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profile = cls.get_profile(profile_name)
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# 构建基础命令
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cmd = [
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"ffmpeg",
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"-hide_banner",
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"-loglevel", "error",
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]
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# 添加硬件加速参数
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if profile.hwaccel_enabled and profile.hwaccel_type:
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if profile.hwaccel_type == "auto":
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# 自动检测硬件加速
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try:
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from app.utils import ffmpeg_utils
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hw_args = ffmpeg_utils.get_ffmpeg_hwaccel_args()
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cmd.extend(hw_args)
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except Exception:
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pass
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elif profile.hwaccel_type == "nvenc_pure":
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# 纯 NVENC 编码器，不使用硬件解码
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pass
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else:
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# 指定的硬件加速类型
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cmd.extend(["-hwaccel", profile.hwaccel_type])
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# 添加输入参数
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cmd.extend([
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"-ss", str(timestamp),
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"-i", input_path,
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"-vframes", "1",
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])
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# 添加质量和格式参数
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if profile.encoder == "libx264":
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cmd.extend(["-q:v", "2"])
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elif profile.encoder == "h264_nvenc":
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cmd.extend(["-cq", "23"])
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elif profile.encoder == "h264_videotoolbox":
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cmd.extend(["-q:v", "65"])
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else:
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cmd.extend(["-q:v", "2"])
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# 添加像素格式
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cmd.extend(["-pix_fmt", profile.pixel_format])
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# 添加额外参数
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cmd.extend(profile.additional_args)
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# 添加输出参数
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cmd.extend(["-y", output_path])
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return cmd
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@classmethod
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def list_profiles(cls) -> Dict[str, str]:
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"""
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列出所有可用的配置文件
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Returns:
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Dict[str, str]: 配置文件名称到描述的映射
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"""
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return {name: profile.description for name, profile in cls.PROFILES.items()}
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@classmethod
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def get_compatibility_report(cls) -> Dict[str, any]:
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"""
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生成兼容性报告
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Returns:
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Dict: 兼容性报告
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"""
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recommended_profile = cls.get_recommended_profile()
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profile = cls.get_profile(recommended_profile)
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try:
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from app.utils import ffmpeg_utils
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hwaccel_info = ffmpeg_utils.get_ffmpeg_hwaccel_info()
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except Exception:
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hwaccel_info = {"available": False, "message": "检测失败"}
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return {
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"system": platform.system(),
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"recommended_profile": recommended_profile,
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"profile_description": profile.description,
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"compatibility_level": profile.compatibility_level,
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"hardware_acceleration": hwaccel_info,
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"suggestions": cls._get_suggestions(profile, hwaccel_info)
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}
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@classmethod
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def _get_suggestions(cls, profile: FFmpegProfile, hwaccel_info: Dict) -> List[str]:
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"""生成优化建议"""
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suggestions = []
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if not hwaccel_info.get("available", False):
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suggestions.append("建议更新显卡驱动以启用硬件加速")
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if profile.compatibility_level >= 4:
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suggestions.append("当前使用高兼容性配置，性能可能较低")
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if platform.system().lower() == "windows" and "nvidia" in hwaccel_info.get("gpu_vendor", "").lower():
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suggestions.append("Windows NVIDIA 用户建议使用纯编码器模式避免滤镜链问题")
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return suggestions
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