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ffmpeg.md

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FFmpeg is a set of open source tools for audio and video processing, such as creating, converting/transcoding, and publishing media content.

Audio/Video Codecs

The FFmpeg docker images are compiled with the following audio and video codecs:

Codec Version Codec Version
fdk-acc 0.1.6 x265 2.9
mp3lame 3.100 vpx 1.7.0
opus 1.2.1 aom 1.0.0
ogg 1.3.3 SVT-HEVC 1.3.0
vorbis 1.3.6 SVT-AV1 custom
x264 stable SVT-VP9 custom

Patches

The FFmpeg builds included the following patches for feature enhancement, better performance or bug fixes:

Patch Description
11625 Enhance 1:N transcoding performance.
11035 Fix libvpx to run on Intel(R) Xeon(R) processors.
H.265 FLV Support H.265 in FLV for RTMP streaming.
IE_FILTERS_01 Intel inference engine detection filter.
IE_FILTERS_02 New filter to do inference classify.
IE_FILTERS_03 IE metadata convertor muxer.
IE_FILTERS_04 Kafka protocol producer.
IE_FILTERS_05 Support object detection and featured face identification.
IE_FILTERS_06 Send metadata in a packet and refine the json format.
IE_FILTERS_07 Refine features of IE filters.
IE_FILTERS_08 Fixed extra comma in iemetadata.
IE_FILTERS_09 Add source as option source url calculate nano times.
IE_FILTERS_10 Fixed buffer overflow issue in iemetadata.
IE_FILTERS_11 Add RGBP pixel format
IE_FILTERS_12 Add more devices into target.
IE_FILTERS_13 Enable vaapi scale for IE inference filters.
IE_FILTERS_14 Iemetadata it will provide data frame by frame.
IE_FILTERS_15 Add libcjson for model pre/post processing.
IE_FILTERS_16 Change IE filters to use model proc.

GPU Acceleration

In GPU images, the FFmpeg docker images are accelerated through vaapi and/or qsv (Intel Media SDK).

FFmpeg Examples:

Transcode raw yuv420 content to SVT-HEVC and mp4:

ffmpeg -f rawvideo -vcodec rawvideo -s 320x240 -r 30 -pix_fmt yuv420p -i test.yuv -c:v libsvt_hevc -y test.mp4

1:N Transcoding:

ffmpeg -i input.h264 -vf "scale=1280:720" -pix_fmt nv12 -f null /dev/null -vf "scale=720:480" -pix_fmt nv12 -f null /dev/null -abr_pipeline

Encoding/decoding with vaapi:

ffmpeg -y -vaapi_device /dev/dri/renderD128 -f rawvideo -video_size 320x240 -r 30 -i test.yuv -vf 'format=nv12, hwupload' -c:v h264_vaapi -y test.mp4
ffmpeg -hwaccel vaapi -hwaccel_device /dev/dri/renderD128 -i test.mp4 -f null /dev/null

Encoding/decoding with qsv (Intel Media SDK):

ffmpeg -y -init_hw_device qsv=hw -filter_hw_device hw -f rawvideo -pix_fmt yuv420p -s:v 320x240 -i test.yuv -vf hwupload=extra_hw_frames=64,format=qsv -c:v h264_qsv -b:v 5M test.mp4
ffmpeg -hwaccel qsv -c:v h264_qsv -i test.mp4 -f null /dev/null

Face detection and emotion identification, save metadata to json format:

ffmpeg -i ~/Videos/xxx.mp4 -vf detect=model=./face-detection-adas-0001/FP32/face-detection-adas-0001.xml, \
classify=model=./emotions_recognition/emotions-recognition-retail-0003.xml:model_proc=emotions-recognition-retail-0003.json \
-an -f iemetadata -source_url $URL -custom_tag $TAG emotion-meta.json

Object Detection:

ffmpeg -i ~/Videos/xxx.mp4 -vf detect=model=./mobilenet-ssd.xml:model_proc=mobilenet-ssd.json -an -f null /dev/null

Face detection and reidentification:

ffmpeg -i ~/Videos/xxx.mp4 -vf detect=model=./face-detection-retail-0004.xml, \
classify=model=./face-reidentification-retail-0095.xml:label=./labels.txt:name=face_id:feature_file=./registered_faces.bin -an -f null /dev/nul

GPU decdoe + face detection

ffmpeg -flags unaligned -hwaccel vaapi -hwaccel_output_format vaapi -hwaccel_device /dev/dri/renderD128 \
# uncomment to choose different devices: CPU=2 GPU=3 VPU=5 HDDL=6
#-i $STREAM -vf "detect=model=$D_FACE_RT_MODEL:device=$CPU" -an -f null - \
#-i $STREAM -vf "detect=model=$D_FACE_RT_FP16_MODEL:device=$GPU" -an -f null -
#-i $STREAM -vf "detect=model=$D_FACE_RT_FP16_MODEL:device=$VPU" -an -f null -
#-i $STREAM -vf "detect=model=$D_FACE_RT_FP16_MODEL:device=$HDDL" -an -f null -