使用GPU硬件加速FFmpeg视频转码及排坑-创新互联

Author: dongdong

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本文内容包括:

  • 在Linux环境下安装FFmpeg
  • 通过命令行实现视频格式识别和转码
  • 有Nvidia显卡的情况下,在Linux下使用GPU进行视频转码加速的方法

FFmpeg编译安装

在FFmpeg官网https://ffmpeg.org/download.html可以下载到ubunto/debian的发行包,其他Linux发行版需自行编译。同时,如果要使用GPU进行硬件加速的话,也是必须自己编译FFmpeg的,所以本节将介绍从源码编译安装FFmpeg的方法(基于RHEL/Centos)

安装依赖工具

yum install autoconf automake bzip2 cmake freetype-devel gcc gcc-c++ git libtool make mercurial pkgconfig zlib-devel

准备工作

在$HOME下创建ffmpeg_sources目录

我自己定义的HOME=/home/local/

编译并安装依赖库

本节中的依赖库基本都是必须的,建议全部安装

nasm

汇编编译器,编译某些依赖库的时候需要

cd /home/local/ffmpeg_sources
curl -O -L http://www.nasm.us/pub/nasm/releasebuilds/2.13.02/nasm-2.13.02.tar.bz2
tar xjvf nasm-2.13.02.tar.bz2
cd nasm-2.13.02
./autogen.sh
./configure --prefix="/home/local/ffmpeg_build" --bindir="/home/local/bin"
make
make install
yasm

汇编编译器,编译某些依赖库的时候需要

cd /home/local/ffmpeg_sources
curl -O -L http://www.tortall.net/projects/yasm/releases/yasm-1.3.0.tar.gz
tar xzvf yasm-1.3.0.tar.gz
cd yasm-1.3.0
./configure --prefix="/home/local/ffmpeg_build" --bindir="/home/local/bin"
make
make install
libx264

H.264视频编码器,如果需要输出H.264编码的视频就需要此库,所以可以说是必备

cd /home/local/ffmpeg_sources
git clone --depth 1 http://git.videolan.org/git/x264
cd x264
PKG_CONFIG_PATH="/home/local/ffmpeg_build/lib/pkgconfig" ./configure --prefix="/home/local/ffmpeg_build" --bindir="/home/local/bin" --enable-static
make
make install
libx265

H.265/HEVC视频编码器。
如果不需要此编码器,可以跳过,并在ffmpeg的configure命令中移除--enable-libx265

cd /home/local/ffmpeg_sources
hg clone https://bitbucket.org/multicoreware/x265
cd ~/ffmpeg_sources/x265/build/linux
cmake -G "Unix Makefiles" -DCMAKE_INSTALL_PREFIX="/home/local/ffmpeg_build" -DENABLE_SHARED:bool=off ../../source
make
make install
libfdk_acc

AAC音频编码器,必备

cd /home/local/ffmpeg_sources
git clone --depth 1 --branch v0.1.6 https://github.com/mstorsjo/fdk-aac.git
cd fdk-aac
autoreconf -fiv
./configure --prefix="/home/local/ffmpeg_build" --disable-shared
make
make install
libmp3lame

MP3音频编码器,必备

cd /home/local/ffmpeg_sources
curl -O -L http://downloads.sourceforge.net/project/lame/lame/3.100/lame-3.100.tar.gz
tar xzvf lame-3.100.tar.gz
cd lame-3.100
./configure --prefix="/home/local/ffmpeg_build" --bindir="/home/local/bin" --disable-shared --enable-nasm
make
make install
libops

OPUS音频编码器
如果不需要此编码器,可以跳过,并在ffmpeg的configure命令中移除--enable-libopus

cd /home/local/ffmpeg_sources
curl -O -L https://archive.mozilla.org/pub/opus/opus-1.2.1.tar.gz
tar xzvf opus-1.2.1.tar.gz
cd opus-1.2.1
./configure --prefix="/home/local/ffmpeg_build" --disable-shared
make
make install
libogg

被libvorbis依赖

cd /home/local/ffmpeg_sources
curl -O -L http://downloads.xiph.org/releases/ogg/libogg-1.3.3.tar.gz
tar xzvf libogg-1.3.3.tar.gz
cd libogg-1.3.3
./configure --prefix="/home/local/ffmpeg_build" --disable-shared
make
make install
libvorbis

Vorbis音频编码器
如果不需要此编码器,可以跳过,并在ffmpeg的configure命令中移除--enable-libvorbis

cd /home/local/ffmpeg_sources
curl -O -L http://downloads.xiph.org/releases/vorbis/libvorbis-1.3.5.tar.gz
tar xzvf libvorbis-1.3.5.tar.gz
cd libvorbis-1.3.5
./configure --prefix="/home/local/ffmpeg_build" --with-ogg="/home/local/ffmpeg_build" --disable-shared
make
make install
libvpx

VP8/VP9视频编/解码器
如果不需要此编/解码器,可以跳过,并在ffmpeg的configure命令中移除--enable-libvpx

cd /home/local/ffmpeg_sources
git clone --depth 1 https://github.com/webmproject/libvpx.git
cd libvpx
./configure --prefix="/home/local/ffmpeg_build" --disable-examples --disable-unit-tests --enable-vp9-highbitdepth --as=yasm
make
make install

编译安装ffmpeg 3.3.8

cd  /home/local/ffmpeg_sources
curl -O -L https://ffmpeg.org/releases/ffmpeg-3.3.8.tar.bz2
tar xjvf ffmpeg-3.3.8.tar.bz2
cd ffmpeg-3.3.8
PATH="/home/local/ffmpeg_sources/bin:$PATH" PKG_CONFIG_PATH="/home/local/ffmpeg_sources/ffmpeg_build/lib/pkgconfig" ./configure   --prefix="/home/local/ffmpeg_sources/ffmpeg_build" --pkg-config-flags="--static"   --extra-cflags="-I /home/local/ffmpeg_sources/ffmpeg_build/include"   --extra-ldflags="-L /home/local/ffmpeg_sources/ffmpeg_build/lib"   --extra-libs=-lpthread   --extra-libs=-lm   --bindir="$HOME/bin"   --enable-gpl   --enable-libfdk_aac   --enable-libfreetype   --enable-libmp3lame   --enable-libopus   --enable-libvorbis   --enable-libvpx   --enable-libx264   --enable-libx265   --enable-nonfree  --enable-libfreetype
make
make install 
hash -r

验证安装

ffmpeg -h

使用FFmpeg

识别视频信息

通过ffprobe命令识别并输出视频信息

ffprobe -v error -show_streams -print_format json   

为方便程序解析,将视频信息输出为json格式,样例如下:

{
    "streams": [
        {
            "index": 0,
            "codec_name": "h364",
            "codec_long_name": "H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10",
            "profile": "High",
            "codec_type": "video",
            "codec_time_base": "61127/3668400",
            "codec_tag_string": "avc1",
            "codec_tag": "0x31637661",
            "width": 1920,
            "height": 1080,
            "coded_width": 1920,
            "coded_height": 1080,
            "has_b_frames": 0,
            "sample_aspect_ratio": "0:1",
            "display_aspect_ratio": "0:1",
            "pix_fmt": "yuv420p",
            "level": 40,
            "color_range": "tv",
            "color_space": "bt709",
            "color_transfer": "bt709",
            "color_primaries": "bt709",
            "chroma_location": "left",
            "refs": 1,
            "is_avc": "true",
            "nal_length_size": "4",
            "r_frame_rate": "30/1",
            "avg_frame_rate": "1834200/61127",
            "time_base": "1/600",
            "start_pts": 0,
            "start_time": "0.000000",
            "duration_ts": 61127,
            "duration": "101.878333",
            "bit_rate": "16279946",
            "bits_per_raw_sample": "8",
            "nb_frames": "3057",
            "disposition": {
                "default": 1,
                "dub": 0,
                "original": 0,
                "comment": 0,
                "lyrics": 0,
                "karaoke": 0,
                "forced": 0,
                "hearing_impaired": 0,
                "visual_impaired": 0,
                "clean_effects": 0,
                "attached_pic": 0,
                "timed_thumbnails": 0
            },
            "tags": {
                "rotate": "90",
                "creation_time": "2018-08-09T09:13:33.000000Z",
                "language": "und",
                "handler_name": "Core Media Data Handler",
                "encoder": "H.264"
            },
            "side_data_list": [
                {
                    "side_data_type": "Display Matrix",
                    "displaymatrix": "\n00000000:            0       65536           0\n00000001:       -65536           0           0\n00000002:     70778880           0  1073741824\n",
                    "rotation": -90
                }
            ]
        },
        {
            "index": 1,
            "codec_name": "aac",
            "codec_long_name": "AAC (Advanced Audio Coding)",
            "profile": "LC",
            "codec_type": "audio",
            "codec_time_base": "1/44100",
            "codec_tag_string": "mp4a",
            "codec_tag": "0x6134706d",
            "sample_fmt": "fltp",
            "sample_rate": "44100",
            "channels": 1,
            "channel_layout": "mono",
            "bits_per_sample": 0,
            "r_frame_rate": "0/0",
            "avg_frame_rate": "0/0",
            "time_base": "1/44100",
            "start_pts": 0,
            "start_time": "0.000000",
            "duration_ts": 4492835,
            "duration": "101.878345",
            "bit_rate": "91595",
            "max_bit_rate": "96000",
            "nb_frames": "4390",
            "disposition": {
                "default": 1,
                "dub": 0,
                "original": 0,
                "comment": 0,
                "lyrics": 0,
                "karaoke": 0,
                "forced": 0,
                "hearing_impaired": 0,
                "visual_impaired": 0,
                "clean_effects": 0,
                "attached_pic": 0,
                "timed_thumbnails": 0
            },
            "tags": {
                "creation_time": "2018-08-09T09:13:33.000000Z",
                "language": "und",
                "handler_name": "Core Media Data Handler"
            }
        },
        {
            "index": 2,
            "codec_type": "data",
            "codec_tag_string": "mebx",
            "codec_tag": "0x7862656d",
            "r_frame_rate": "0/0",
            "avg_frame_rate": "0/0",
            "time_base": "1/600",
            "start_pts": 0,
            "start_time": "0.000000",
            "duration_ts": 61127,
            "duration": "101.878333",
            "bit_rate": "119",
            "nb_frames": "17",
            "disposition": {
                "default": 1,
                "dub": 0,
                "original": 0,
                "comment": 0,
                "lyrics": 0,
                "karaoke": 0,
                "forced": 0,
                "hearing_impaired": 0,
                "visual_impaired": 0,
                "clean_effects": 0,
                "attached_pic": 0,
                "timed_thumbnails": 0
            },
            "tags": {
                "creation_time": "2018-08-09T09:13:33.000000Z",
                "language": "und",
                "handler_name": "Core Media Data Handler"
            }
        },
        {
            "index": 3,
            "codec_type": "data",
            "codec_tag_string": "mebx",
            "codec_tag": "0x7862656d",
            "r_frame_rate": "0/0",
            "avg_frame_rate": "0/0",
            "time_base": "1/600",
            "start_pts": 0,
            "start_time": "0.000000",
            "duration_ts": 61127,
            "duration": "101.878333",
            "nb_frames": "1",
            "disposition": {
                "default": 1,
                "dub": 0,
                "original": 0,
                "comment": 0,
                "lyrics": 0,
                "karaoke": 0,
                "forced": 0,
                "hearing_impaired": 0,
                "visual_impaired": 0,
                "clean_effects": 0,
                "attached_pic": 0,
                "timed_thumbnails": 0
            },
            "tags": {
                "creation_time": "2018-08-09T09:13:33.000000Z",
                "language": "und",
                "handler_name": "Core Media Data Handler"
            }
        }
    ]
}

可以看到一共返回了4个流,其中第0个是视频流,1是音频流,2和3是附加数据,没什么用
如果想指定分析视频流或音频流的话,可以加上参数-show_streams -v-show_streams -a,这样就会只输出视频/音频流的分析结果

视频转码

ffmpeg -i  -c:v libx264 -b:v 2048k -vf scale=1280:-1 -y 

上述命令将输入视频转码为h364编码的视频

  • -c:v:指定编码器,编码器列表可以使用ffmpeg -codecs查看
  • -vf scale:指定输出视频的宽高,高-1代表按照比例自动适应
  • -b:v:指定输出视频的码率,即输出视频每秒的bit数
  • libx264支持的其他参数请使用ffmpeg -h encoder=libx264命令查询,如转码为其他编码,也可使用类似命令查询可用参数

使用Nvidia显卡GPU进行转码

重头戏来了,这块的资料相当少,我也是费了一番力气才搞定

CUDA

CUDA是Nvidia出的一个GPU计算库,让程序员可以驱动Nvidia显卡的GPU进行各种工作,其中就包含了视频的编解码

安装CUDA

首先验证一下显卡驱动是否装好

nvidia-smi

如果驱动正常的话,此命令会输出显卡的型号、驱动版本、现存/GPU占用等信息。如何安装显卡驱动本文不描述,请参考其他资料。

到CUDA官网https://developer.nvidia.com/cuda-downloads下载对应平台的发行包,这里我选择

wget http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-rhel7-10-2-local-10.2.89-440.33.01-1.0-1.x86_64.rpm
sudo rpm -i cuda-repo-rhel7-10-2-local-10.2.89-440.33.01-1.0-1.x86_64.rpm
sudo yum clean all
sudo yum -y install nvidia-driver-latest-dkms cuda
sudo yum -y install cuda-drivers

一共大概要安装90多个依赖库,注意一下安装完成后的报告,我首次安装时有一个库不知道为什么安装失败了,又单独yum install了该库一次才成功

验证安装
/usr/local/cuda-9.2/bin/nvcc -V

安装成功的话,会输出类似文本:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Tue_Jun_12_23:07:04_CDT_2018
Cuda compilation tools, release 9.2, V9.2.148

重新编译ffmpeg

要让ffmpeg能够使用CUDA提供的GPU编解码器,必须重新编译ffmpeg,让其能够通过动态链接调用CUDA的能力

首先要编译安装nv-codec-headers库

git clone https://git.videolan.org/git/ffmpeg/nv-codec-headers.git
make PREFIX="/home/local/ffmpeg_build" BINDDIR="/home/local/bin"
make install PREFIX="/home/local/ffmpeg_build" BINDDIR="/home/local/bin" 

进入/home/local/ffmepg_sources/ffmpeg-3.3.8/目录重新执行ffmpeg的编译和安装
注意configure命令参数和之前configure命令参数的区别

PATH="/home/local/ffmpeg_sources/bin:$PATH" PKG_CONFIG_PATH="/home/local/ffmpeg_sources/ffmpeg_build/lib/pkgconfig" ./configure   --prefix="/home/local/ffmpeg_sources/ffmpeg_build" --pkg-config-flags="--static"   --extra-cflags="-I /home/local/ffmpeg_sources/ffmpeg_build/include -I/usr/local/cuda/include"   --extra-ldflags="-L /home/local/ffmpeg_sources/ffmpeg_build/lib  -L/usr/local/cuda/lib64"   --extra-libs=-lpthread   --extra-libs=-lm   --bindir="$HOME/bin"   --enable-gpl   --enable-libfdk_aac   --enable-libfreetype   --enable-libmp3lame   --enable-libopus   --enable-libvorbis   --enable-libvpx   --enable-libx264   --enable-libx265   --enable-nonfree  --enable-libfreetype --enable-cuda --enable-cuvid  --enable-nvenc --enable-libnpp
验证安装

重新安装完ffmpeg,使用ffmpeg -hwaccels命令查看支持的硬件加速选项

Hardware acceleration methods:
cuvid

可以看到多出来一种叫做cuvid的硬件加速选项,这就是CUDA提供的GPU视频编解码加速选项

然后查看cuvid提供的GPU编解码器ffmpeg -codecs | grep cuvid

 DEV.LS h364                 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10 (decoders: h364 h364_cuvid ) (encoders: libx264 libx264rgb h364_nvenc nvenc nvenc_h364 )
 DEV.L. hevc                 H.265 / HEVC (High Efficiency Video Coding) (decoders: hevc hevc_cuvid ) (encoders: libx265 nvenc_hevc hevc_nvenc )
 DEVIL. mjpeg                Motion JPEG (decoders: mjpeg mjpeg_cuvid )
 DEV.L. mpeg1video           MPEG-1 video (decoders: mpeg1video mpeg1_cuvid )
 DEV.L. mpeg2video           MPEG-2 video (decoders: mpeg2video mpegvideo mpeg2_cuvid )
 DEV.L. mpeg4                MPEG-4 part 2 (decoders: mpeg4 mpeg4_cuvid )
 D.V.L. vc1                  SMPTE VC-1 (decoders: vc1 vc1_cuvid )
 DEV.L. vp8                  On2 VP8 (decoders: vp8 libvpx vp8_cuvid ) (encoders: libvpx )
 DEV.L. vp9                  Google VP9 (decoders: vp9 libvpx-vp9 vp9_cuvid ) (encoders: libvpx-vp9 )

所有带有"cuvid"或"nvenc"的,都是CUDA提供的GPU编解码器
可以看到,我们现在可以进行h364/hevc/mjpeg/mpeg1/mpeg2/mpeg4/vc1/vp8/vp9格式的GPU解码,以及h364/hevc格式的GPU编码

使用GPU进行视频转码

用GPU进行转码的命令和软转码命令不太一样,CPU转码的时候,我们可以依赖ffmpeg识别输入视频的编码格式并选择对应的解码器,但ffmpeg只会自动选择CPU解码器,要让ffmpeg使用GPU解码器,必须先用ffprobe识别出输入视频的编码格式,然后在命令行中指定对应的GPU解码器。

例如,将h364编码的源视频转码为指定尺寸和码率的h364编码视频:

ffmpeg -hwaccel cuvid -c:v h364_cuvid -i  -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y 
  • -hwaccel cuvid:指定使用cuvid硬件加速
  • -c:v h364_cuvid:使用h364_cuvid进行视频解码
  • -c:v h364_nvenc:使用h364_nvenc进行视频编码
  • -vf scale_npp=1280:-1:指定输出视频的宽高,注意,这里和软解码时使用的-vf scale=x:x不一样

转码期间使用nvidia-smi查看显卡状态,能够看到ffmpeg确实是在使用GPU进行转码:

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     62543      C   ffmpeg                                       193MiB |
+-----------------------------------------------------------------------------+

GPU转码效率测试

在配有两颗Intel-E5-2630v3 CPU和两块Nvidia Tesla M4显卡的服务器上,进行h364视频转码测试,成绩如下:

  • GPU转码平均耗时:8s
  • CPU转码平均耗时:25s

并行转码时,CPU软转的效率有所提高,3个转码任务并行时32颗核心全被占满,此时的成绩

  • GPU转码平均耗时:8s
  • CPU转码平均耗时:18s

不难看出,并行时GPU的转码速度并没有提高,可见一颗GPU同时只能执行一个转码任务。那么,如果服务器上插有多块显卡,ffmpeg是否会使用多颗GPU进行并行转码呢?

很遗憾,答案是否。

ffmpeg并不具备自动向不同GPU分配转码任务的能力,但经过一番调查后,发现可以通过-hwaccel_device参数指定转码任务使用的GPU!

向不同GPU提交转码任务

ffmpeg -hwaccel cuvid -hwaccel_device 0 -c:v h364_cuvid -i  -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y 
ffmpeg -hwaccel cuvid -hwaccel_device 1 -c:v h364_cuvid -i  -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y 
  • -hwaccel_device N:指定某颗GPU执行转码任务,N为数字

此时nvidia-smi显示:

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     96931      C   ffmpeg                                       193MiB |
|    1     96930      C   ffmpeg                                       193MiB |
+-----------------------------------------------------------------------------+

可以进行并行GPU转码了!

那么在占满服务器资源时,GPU转码和CPU转码的效率如下:

  • GPU转码平均耗时:4s
  • CPU转码平均耗时:18s

GPU效率是CPU的4.5倍

排坑

一、错误: ./autogen.sh: 4: ./autogen.sh: autoreconf: not found

解决:yum -y install autoconf  automake libtool  
然后执行

二、 错误:ERROR: freetype2 not found using pkg-config

yum install -y bzip2
wget http://download.savannah.gnu.org/releases/freetype/freetype-2.10.0.tar.bz2
tar -jxvf freetype-2.10.0.tar.bz2
cd freetype-2.10.0
./configure --prefix= /home/local/ffmpeg_sources/ffmpeg_build
make 
make install 

三、错误: configure: error: C compiler cannot create executables See `config.log' for more details make: *** [setup] Error 77

解决:先卸载旧的c c++ gcc gcc++

然后重新安装

四、错误:make: *** [libavcodec/libfdk-aacenc.o] Error 1

因本身3.*带有BUG换成4.1 官网下载地址

(此步骤也可以使用git clone下载源码包,本质上是一样的 )

然后重新编译即可。

参考链接:https://www.jianshu.com/p/59da3d350488

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