mirror of
https://github.com/linyqh/NarratoAI.git
synced 2025-12-12 03:02:48 +00:00
514 lines
20 KiB
Python
514 lines
20 KiB
Python
"""
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FFmpeg 工具模块 - 提供 FFmpeg 相关的工具函数，特别是硬件加速检测
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"""
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import os
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import platform
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import subprocess
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from typing import Dict, List, Optional, Tuple, Union
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from loguru import logger
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# 全局变量，存储检测到的硬件加速信息
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_FFMPEG_HW_ACCEL_INFO = {
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"available": False,
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"type": None,
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"encoder": None,
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"hwaccel_args": [],
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"message": "",
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"is_dedicated_gpu": False
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}
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def check_ffmpeg_installation() -> bool:
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"""
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检查ffmpeg是否已安装
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Returns:
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bool: 如果安装则返回True，否则返回False
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"""
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try:
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# 在Windows系统上使用UTF-8编码
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is_windows = os.name == 'nt'
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if is_windows:
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subprocess.run(['ffmpeg', '-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8', check=True)
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else:
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subprocess.run(['ffmpeg', '-version'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True)
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return True
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except (subprocess.SubprocessError, FileNotFoundError):
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logger.error("ffmpeg未安装或不在系统PATH中，请安装ffmpeg")
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return False
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def detect_hardware_acceleration() -> Dict[str, Union[bool, str, List[str], None]]:
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"""
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检测系统可用的硬件加速器，并存储结果到全局变量
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Returns:
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Dict: 包含硬件加速信息的字典
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"""
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global _FFMPEG_HW_ACCEL_INFO
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# 如果已经检测过，直接返回结果
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if _FFMPEG_HW_ACCEL_INFO["type"] is not None:
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return _FFMPEG_HW_ACCEL_INFO
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# 检查ffmpeg是否已安装
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if not check_ffmpeg_installation():
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_FFMPEG_HW_ACCEL_INFO["message"] = "FFmpeg未安装或不在系统PATH中"
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return _FFMPEG_HW_ACCEL_INFO
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# 检测操作系统
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system = platform.system().lower()
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logger.debug(f"检测硬件加速 - 操作系统: {system}")
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# 获取FFmpeg支持的硬件加速器列表
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try:
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# 在Windows系统上使用UTF-8编码
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is_windows = os.name == 'nt'
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if is_windows:
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hwaccels_cmd = subprocess.run(
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['ffmpeg', '-hide_banner', '-hwaccels'],
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stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8', text=True
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)
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else:
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hwaccels_cmd = subprocess.run(
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['ffmpeg', '-hide_banner', '-hwaccels'],
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stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
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)
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supported_hwaccels = hwaccels_cmd.stdout.lower()
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except Exception as e:
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logger.error(f"获取FFmpeg硬件加速器列表失败: {str(e)}")
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supported_hwaccels = ""
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# 根据操作系统检测不同的硬件加速器
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if system == 'darwin': # macOS
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_detect_macos_acceleration(supported_hwaccels)
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elif system == 'windows': # Windows
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_detect_windows_acceleration(supported_hwaccels)
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elif system == 'linux': # Linux
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_detect_linux_acceleration(supported_hwaccels)
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else:
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logger.warning(f"不支持的操作系统: {system}")
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_FFMPEG_HW_ACCEL_INFO["message"] = f"不支持的操作系统: {system}"
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# 记录检测结果已经在启动时输出，这里不再重复输出
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return _FFMPEG_HW_ACCEL_INFO
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def _detect_macos_acceleration(supported_hwaccels: str) -> None:
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"""
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检测macOS系统的硬件加速
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Args:
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supported_hwaccels: FFmpeg支持的硬件加速器列表
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"""
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global _FFMPEG_HW_ACCEL_INFO
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if 'videotoolbox' in supported_hwaccels:
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# 测试videotoolbox
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "videotoolbox", "-i", "/dev/null", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, text=True, check=False
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)
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "videotoolbox"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_videotoolbox"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "videotoolbox"]
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# macOS的Metal GPU加速通常是集成GPU
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = False
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return
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except Exception as e:
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logger.debug(f"测试videotoolbox失败: {str(e)}")
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_FFMPEG_HW_ACCEL_INFO["message"] = "macOS系统未检测到可用的videotoolbox硬件加速"
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def _detect_windows_acceleration(supported_hwaccels: str) -> None:
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"""
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检测Windows系统的硬件加速
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Args:
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supported_hwaccels: FFmpeg支持的硬件加速器列表
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"""
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global _FFMPEG_HW_ACCEL_INFO
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# 在Windows上，首先检查显卡信息
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gpu_info = _get_windows_gpu_info()
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# 检查是否为AMD显卡
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if 'amd' in gpu_info.lower() or 'radeon' in gpu_info.lower():
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logger.info("检测到AMD显卡，为避免兼容性问题，将使用软件编码")
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_FFMPEG_HW_ACCEL_INFO["message"] = "检测到AMD显卡，为避免兼容性问题，将使用软件编码"
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return
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# 检查是否为Intel集成显卡
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is_intel_integrated = False
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if 'intel' in gpu_info.lower() and ('hd graphics' in gpu_info.lower() or 'uhd graphics' in gpu_info.lower()):
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logger.info("检测到Intel集成显卡")
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is_intel_integrated = True
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# 检测NVIDIA CUDA支持
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if 'cuda' in supported_hwaccels and 'nvidia' in gpu_info.lower():
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# 添加调试日志
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logger.debug(f"Windows检测到NVIDIA显卡，尝试CUDA加速")
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try:
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# 先检查NVENC编码器是否可用，使用UTF-8编码
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encoders_cmd = subprocess.run(
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["ffmpeg", "-hide_banner", "-encoders"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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has_nvenc = "h264_nvenc" in encoders_cmd.stdout.lower()
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logger.debug(f"NVENC编码器检测结果: {'可用' if has_nvenc else '不可用'}")
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# 测试CUDA硬件加速，使用UTF-8编码
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "cuda", "-i", "NUL", "-f", "null", "-t", "0.1", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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# 记录详细的返回信息以便调试
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logger.debug(f"CUDA测试返回码: {test_cmd.returncode}")
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logger.debug(f"CUDA测试错误输出: {test_cmd.stderr[:200]}..." if len(test_cmd.stderr) > 200 else f"CUDA测试错误输出: {test_cmd.stderr}")
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if test_cmd.returncode == 0 or has_nvenc:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "cuda"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_nvenc"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "cuda"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = True
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return
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# 如果上面的测试失败，尝试另一种方式，使用UTF-8编码
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test_cmd2 = subprocess.run(
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["ffmpeg", "-hide_banner", "-loglevel", "error", "-hwaccel", "cuda", "-hwaccel_output_format", "cuda", "-i", "NUL", "-f", "null", "-t", "0.1", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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if test_cmd2.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "cuda"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_nvenc"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "cuda", "-hwaccel_output_format", "cuda"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = True
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return
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except Exception as e:
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logger.debug(f"测试CUDA失败: {str(e)}")
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# 检测Intel QSV支持（如果是Intel显卡）
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if 'qsv' in supported_hwaccels and 'intel' in gpu_info.lower():
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "qsv", "-i", "/dev/null", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, text=True, check=False
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)
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "qsv"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_qsv"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "qsv"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = not is_intel_integrated
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return
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except Exception as e:
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logger.debug(f"测试QSV失败: {str(e)}")
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# 检测D3D11VA支持
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if 'd3d11va' in supported_hwaccels:
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logger.debug("Windows尝试D3D11VA加速")
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "d3d11va", "-i", "NUL", "-f", "null", "-t", "0.1", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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# 记录详细的返回信息以便调试
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logger.debug(f"D3D11VA测试返回码: {test_cmd.returncode}")
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "d3d11va"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264" # D3D11VA只用于解码，编码仍使用软件编码器
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "d3d11va"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = not is_intel_integrated
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return
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except Exception as e:
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logger.debug(f"测试D3D11VA失败: {str(e)}")
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# 检测DXVA2支持
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if 'dxva2' in supported_hwaccels:
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logger.debug("Windows尝试DXVA2加速")
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "dxva2", "-i", "NUL", "-f", "null", "-t", "0.1", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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# 记录详细的返回信息以便调试
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logger.debug(f"DXVA2测试返回码: {test_cmd.returncode}")
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "dxva2"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264" # DXVA2只用于解码，编码仍使用软件编码器
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "dxva2"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = not is_intel_integrated
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return
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except Exception as e:
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logger.debug(f"测试DXVA2失败: {str(e)}")
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# 如果检测到NVIDIA显卡但前面的测试都失败，尝试直接使用NVENC编码器
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if 'nvidia' in gpu_info.lower():
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logger.debug("Windows检测到NVIDIA显卡，尝试直接使用NVENC编码器")
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try:
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# 检查NVENC编码器是否可用，使用UTF-8编码
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encoders_cmd = subprocess.run(
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["ffmpeg", "-hide_banner", "-encoders"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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if "h264_nvenc" in encoders_cmd.stdout.lower():
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logger.debug("NVENC编码器可用，尝试直接使用")
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# 测试NVENC编码器，使用UTF-8编码
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test_cmd = subprocess.run(
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["ffmpeg", "-f", "lavfi", "-i", "color=c=black:s=640x360:r=30", "-c:v", "h264_nvenc", "-t", "0.1", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, encoding='utf-8', text=True, check=False
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)
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logger.debug(f"NVENC编码器测试返回码: {test_cmd.returncode}")
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "nvenc"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_nvenc"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = [] # 不使用hwaccel参数，直接使用编码器
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = True
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return
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except Exception as e:
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logger.debug(f"测试NVENC编码器失败: {str(e)}")
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_FFMPEG_HW_ACCEL_INFO["message"] = f"Windows系统未检测到可用的硬件加速，显卡信息: {gpu_info}"
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def _detect_linux_acceleration(supported_hwaccels: str) -> None:
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"""
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检测Linux系统的硬件加速
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Args:
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supported_hwaccels: FFmpeg支持的硬件加速器列表
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"""
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global _FFMPEG_HW_ACCEL_INFO
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# 获取Linux显卡信息
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gpu_info = _get_linux_gpu_info()
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is_nvidia = 'nvidia' in gpu_info.lower()
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is_intel = 'intel' in gpu_info.lower()
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is_amd = 'amd' in gpu_info.lower() or 'radeon' in gpu_info.lower()
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# 检测NVIDIA CUDA支持
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if 'cuda' in supported_hwaccels and is_nvidia:
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "cuda", "-i", "/dev/null", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, text=True, check=False
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)
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "cuda"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_nvenc"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "cuda"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = True
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return
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except Exception as e:
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logger.debug(f"测试CUDA失败: {str(e)}")
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# 检测VAAPI支持
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if 'vaapi' in supported_hwaccels:
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# 检查是否存在渲染设备
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render_devices = ['/dev/dri/renderD128', '/dev/dri/renderD129']
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render_device = None
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for device in render_devices:
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if os.path.exists(device):
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render_device = device
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break
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if render_device:
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "vaapi", "-vaapi_device", render_device,
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"-i", "/dev/null", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, text=True, check=False
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)
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "vaapi"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_vaapi"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "vaapi", "-vaapi_device", render_device]
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# 根据显卡类型判断是否为独立显卡
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = is_nvidia or (is_amd and not is_intel)
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return
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except Exception as e:
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logger.debug(f"测试VAAPI失败: {str(e)}")
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# 检测Intel QSV支持
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if 'qsv' in supported_hwaccels and is_intel:
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try:
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test_cmd = subprocess.run(
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["ffmpeg", "-hwaccel", "qsv", "-i", "/dev/null", "-f", "null", "-"],
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stderr=subprocess.PIPE, stdout=subprocess.PIPE, text=True, check=False
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)
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if test_cmd.returncode == 0:
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_FFMPEG_HW_ACCEL_INFO["available"] = True
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_FFMPEG_HW_ACCEL_INFO["type"] = "qsv"
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_FFMPEG_HW_ACCEL_INFO["encoder"] = "h264_qsv"
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_FFMPEG_HW_ACCEL_INFO["hwaccel_args"] = ["-hwaccel", "qsv"]
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_FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"] = False # Intel QSV通常是集成GPU
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return
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except Exception as e:
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logger.debug(f"测试QSV失败: {str(e)}")
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_FFMPEG_HW_ACCEL_INFO["message"] = f"Linux系统未检测到可用的硬件加速，显卡信息: {gpu_info}"
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def _get_windows_gpu_info() -> str:
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"""
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获取Windows系统的显卡信息
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Returns:
|
||
str: 显卡信息字符串
|
||
"""
|
||
try:
|
||
# 使用PowerShell获取更可靠的显卡信息，并使用UTF-8编码
|
||
gpu_info = subprocess.run(
|
||
['powershell', '-Command', "Get-WmiObject Win32_VideoController | Select-Object Name | Format-List"],
|
||
stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8', text=True, check=False
|
||
)
|
||
|
||
# 如果PowerShell失败，尝试使用wmic
|
||
if not gpu_info.stdout.strip():
|
||
gpu_info = subprocess.run(
|
||
['wmic', 'path', 'win32_VideoController', 'get', 'name'],
|
||
stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8', text=True, check=False
|
||
)
|
||
|
||
# 记录详细的显卡信息以便调试
|
||
logger.debug(f"Windows显卡信息: {gpu_info.stdout}")
|
||
return gpu_info.stdout
|
||
except Exception as e:
|
||
logger.warning(f"获取Windows显卡信息失败: {str(e)}")
|
||
return "Unknown GPU"
|
||
|
||
|
||
def _get_linux_gpu_info() -> str:
|
||
"""
|
||
获取Linux系统的显卡信息
|
||
|
||
Returns:
|
||
str: 显卡信息字符串
|
||
"""
|
||
try:
|
||
# 尝试使用lspci命令
|
||
gpu_info = subprocess.run(
|
||
['lspci', '-v', '-nn', '|', 'grep', '-i', 'vga\\|display'],
|
||
stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, shell=True, check=False
|
||
)
|
||
if gpu_info.stdout:
|
||
return gpu_info.stdout
|
||
|
||
# 如果lspci命令失败，尝试使用glxinfo
|
||
gpu_info = subprocess.run(
|
||
['glxinfo', '|', 'grep', '-i', 'vendor\\|renderer'],
|
||
stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, shell=True, check=False
|
||
)
|
||
if gpu_info.stdout:
|
||
return gpu_info.stdout
|
||
|
||
return "Unknown GPU"
|
||
except Exception as e:
|
||
logger.warning(f"获取Linux显卡信息失败: {str(e)}")
|
||
return "Unknown GPU"
|
||
|
||
|
||
def get_ffmpeg_hwaccel_args() -> List[str]:
|
||
"""
|
||
获取FFmpeg硬件加速参数
|
||
|
||
Returns:
|
||
List[str]: FFmpeg硬件加速参数列表
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO["hwaccel_args"]
|
||
|
||
|
||
def get_ffmpeg_hwaccel_type() -> Optional[str]:
|
||
"""
|
||
获取FFmpeg硬件加速类型
|
||
|
||
Returns:
|
||
Optional[str]: 硬件加速类型，如果不支持则返回None
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO["type"] if _FFMPEG_HW_ACCEL_INFO["available"] else None
|
||
|
||
|
||
def get_ffmpeg_hwaccel_encoder() -> Optional[str]:
|
||
"""
|
||
获取FFmpeg硬件加速编码器
|
||
|
||
Returns:
|
||
Optional[str]: 硬件加速编码器，如果不支持则返回None
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO["encoder"] if _FFMPEG_HW_ACCEL_INFO["available"] else None
|
||
|
||
|
||
def get_ffmpeg_hwaccel_info() -> Dict[str, Union[bool, str, List[str], None]]:
|
||
"""
|
||
获取FFmpeg硬件加速信息
|
||
|
||
Returns:
|
||
Dict: 包含硬件加速信息的字典
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO
|
||
|
||
|
||
def is_ffmpeg_hwaccel_available() -> bool:
|
||
"""
|
||
检查是否有可用的FFmpeg硬件加速
|
||
|
||
Returns:
|
||
bool: 如果有可用的硬件加速则返回True，否则返回False
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO["available"]
|
||
|
||
|
||
def is_dedicated_gpu() -> bool:
|
||
"""
|
||
检查是否使用独立显卡进行硬件加速
|
||
|
||
Returns:
|
||
bool: 如果使用独立显卡则返回True，否则返回False
|
||
"""
|
||
# 如果还没有检测过，先进行检测
|
||
if _FFMPEG_HW_ACCEL_INFO["type"] is None:
|
||
detect_hardware_acceleration()
|
||
|
||
return _FFMPEG_HW_ACCEL_INFO["is_dedicated_gpu"]
|