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Author: dongdong
Email: ldyldy828@126.com
本文內容包括：
在FFmpeg官網https://ffmpeg.org/download.html可以下載到ubunto/debian的發行包，其他Linux發行版需自行編譯。同時，如果要使用GPU進行硬件加速的話，也是必須自己編譯FFmpeg的，所以本節將介紹從源碼編譯安裝FFmpeg的方法（基于RHEL/Centos）
yum install autoconf automake bzip2 cmake freetype-devel gcc gcc-c++ git libtool make mercurial pkgconfig zlib-devel在$HOME下創建ffmpeg_sources目錄
我自己定義的HOME=/home/local/
本節中的依賴庫基本都是必須的，建議全部安裝
匯編編譯器，編譯某些依賴庫的時候需要
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匯編編譯器，編譯某些依賴庫的時候需要
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 installH.264視頻編碼器，如果需要輸出H.264編碼的視頻就需要此庫，所以可以說是必備
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 installH.265/HEVC視頻編碼器。
如果不需要此編碼器，可以跳過，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 installAAC音頻編碼器，必備
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 installMP3音頻編碼器，必備
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 installOPUS音頻編碼器
如果不需要此編碼器，可以跳過，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被libvorbis依賴
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
Vorbis音頻編碼器
如果不需要此編碼器，可以跳過，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
VP8/VP9視頻編/解碼器
如果不需要此編/解碼器，可以跳過，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
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
驗證安裝
ffmpeg -h
通過ffprobe命令識別并輸出視頻信息
ffprobe -v error -show_streams -print_format json <input>
為方便程序解析，將視頻信息輸出為json格式，樣例如下：
{
"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個流，其中第0個是視頻流，1是音頻流，2和3是附加數據，沒什么用
如果想指定分析視頻流或音頻流的話，可以加上參數-show_streams -v或-show_streams -a，這樣就會只輸出視頻/音頻流的分析結果
ffmpeg -i <input> -c:v libx264 -b:v 2048k -vf scale=1280:-1 -y <output>
上述命令將輸入視頻轉碼為h364編碼的視頻
ffmpeg -h encoder=libx264命令查詢，如轉碼為其他編碼，也可使用類似命令查詢可用參數重頭戲來了，這塊的資料相當少，我也是費了一番力氣才搞定
CUDA是Nvidia出的一個GPU計算庫，讓程序員可以驅動Nvidia顯卡的GPU進行各種工作，其中就包含了視頻的編解碼
首先驗證一下顯卡驅動是否裝好
nvidia-smi
如果驅動正常的話，此命令會輸出顯卡的型號、驅動版本、現存/GPU占用等信息。如何安裝顯卡驅動本文不描述，請參考其他資料。
到CUDA官網https://developer.nvidia.com/cuda-downloads下載對應平臺的發行包，這里我選擇
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
一共大概要安裝90多個依賴庫，注意一下安裝完成后的報告，我首次安裝時有一個庫不知道為什么安裝失敗了，又單獨yum install了該庫一次才成功
/usr/local/cuda-9.2/bin/nvcc -V
安裝成功的話，會輸出類似文本：
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
要讓ffmpeg能夠使用CUDA提供的GPU編解碼器，必須重新編譯ffmpeg，讓其能夠通過動態鏈接調用CUDA的能力
首先要編譯安裝nv-codec-headers庫
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"
進入/home/local/ffmepg_sources/ffmpeg-3.3.8/目錄重新執行ffmpeg的編譯和安裝
注意configure命令參數和之前configure命令參數的區別
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
重新安裝完ffmpeg，使用ffmpeg -hwaccels命令查看支持的硬件加速選項
Hardware acceleration methods:
cuvid
可以看到多出來一種叫做cuvid的硬件加速選項，這就是CUDA提供的GPU視頻編解碼加速選項
然后查看cuvid提供的GPU編解碼器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 )
所有帶有"cuvid"或"nvenc"的，都是CUDA提供的GPU編解碼器
可以看到，我們現在可以進行h364/hevc/mjpeg/mpeg1/mpeg2/mpeg4/vc1/vp8/vp9格式的GPU解碼，以及h364/hevc格式的GPU編碼
用GPU進行轉碼的命令和軟轉碼命令不太一樣，CPU轉碼的時候，我們可以依賴ffmpeg識別輸入視頻的編碼格式并選擇對應的解碼器，但ffmpeg只會自動選擇CPU解碼器，要讓ffmpeg使用GPU解碼器，必須先用ffprobe識別出輸入視頻的編碼格式，然后在命令行中指定對應的GPU解碼器。
例如，將h364編碼的源視頻轉碼為指定尺寸和碼率的h364編碼視頻：
ffmpeg -hwaccel cuvid -c:v h364_cuvid -i <input> -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y <output>
轉碼期間使用nvidia-smi查看顯卡狀態，能夠看到ffmpeg確實是在使用GPU進行轉碼：
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 62543 C ffmpeg 193MiB |
+-----------------------------------------------------------------------------+
在配有兩顆Intel-E5-2630v3 CPU和兩塊Nvidia Tesla M4顯卡的服務器上，進行h364視頻轉碼測試，成績如下：
并行轉碼時，CPU軟轉的效率有所提高，3個轉碼任務并行時32顆核心全被占滿，此時的成績
不難看出，并行時GPU的轉碼速度并沒有提高，可見一顆GPU同時只能執行一個轉碼任務。那么，如果服務器上插有多塊顯卡，ffmpeg是否會使用多顆GPU進行并行轉碼呢？
很遺憾，答案是否。
ffmpeg并不具備自動向不同GPU分配轉碼任務的能力，但經過一番調查后，發現可以通過-hwaccel_device參數指定轉碼任務使用的GPU！
ffmpeg -hwaccel cuvid -hwaccel_device 0 -c:v h364_cuvid -i <input> -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y <output>
ffmpeg -hwaccel cuvid -hwaccel_device 1 -c:v h364_cuvid -i <input> -c:v h364_nvenc -b:v 2048k -vf scale_npp=1280:-1 -y <output>
此時nvidia-smi顯示：
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 96931 C ffmpeg 193MiB |
| 1 96930 C ffmpeg 193MiB |
+-----------------------------------------------------------------------------+
可以進行并行GPU轉碼了！
那么在占滿服務器資源時，GPU轉碼和CPU轉碼的效率如下：
GPU效率是CPU的4.5倍
一、錯誤： ./autogen.sh: 4: ./autogen.sh: autoreconf: not found
解決：yum -y install autoconf automake libtool
然后執行
二、 錯誤：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、錯誤： configure: error: C compiler cannot create executables See `config.log' for more details make: *** [setup] Error 77
解決：先卸載舊的c c++ gcc gcc++
然后重新安裝
四、錯誤：make: *** [libavcodec/libfdk-aacenc.o] Error 1
因本身3.*帶有BUG換成4.1 官網下載地址
(此步驟也可以使用git clone下載源碼包，本質上是一樣的 )
然后重新編譯即可。
參考鏈接：https://www.jianshu.com/p/59da3d350488
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