motion verctor estimation, motion vector export, ffmpeg advanced usage

use cases

to detect hard-coded subtitles, crop the region and detect sudden changes

can also use pyscenedetect to do the job

pyav

docs

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pip3 install av

remove/detect silence

… silencedetect A->A Detect silence.

… silenceremove A->A Remove silence.

frame interpolate

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ffmpeg -y -i "/root/Desktop/works/pyjom/tests/random_giphy_gifs/samoyed.gif" \
-vf "minterpolate,scale=w=iw*2:h=ih*2:flags=lanczos,hqdn3d" \
-r 60 ffmpeg_samoyed.mp4

motion estimation

to get mosaic motion vectors and visualize:

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ffmpeg -i "/root/Desktop/works/pyjom/tests/random_giphy_gifs/samoyed.gif" \
-vf "mestimate=epzs:mb_size=16:search_param=7, codecview=mv=pf+bf+bb"  \
mestimate_output.mp4 -y

get help

on specific filter:

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ffmpeg -h filter=showspectrumpic

on all filters:

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ffmpeg -filters

crop detection, picture in picture (PIP) detection

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ffmpeg -i "/root/Desktop/works/pyjom/samples/video/LiEIfnsvn.mp4" \
-vf "mestimate,cropdetect=mode=mvedges,metadata=mode=print" \
-f null -

scene change detection

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ffmpeg -hide_banner -i "$file" -an \
-filter:v \
"select='gt(scene,0.2)',showinfo" \
-f null \
- 2>&1

extract motion vectors

ffmpeg can produce motion vector estimation but it is not exportable, only for internal use.

mp4 format provides motion vector information thus maybe we need not to use GPU to get those ‘optical flow’ data.

extract by using ffmpeg apis

mv-extractor Extract frames and motion vectors from H.264 and MPEG-4 encoded video.

extract from mp4 file

mpegflow for easy extraction of motion vectors stored in video files

mv-tractus: A simple tool to extract motion vectors from h264 encoded videos.

take screenshot at time:

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ffmpeg -ss 01:10:35 -i invideo.mp4 -vframes 1 -q:v 3 screenshot.jpg

video denoise filters:

dctdnoiz fftdnoiz hqdn3d nlmeans owdenoise removegrain vaguedenoiser nlmeans_opencl yaepblur

super-resolution, resampling:

deeplearning model, tensorflow

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env LD_LIBRARY_PATH=/root/anaconda3/pkgs/cudatoolkit-10.0.130-0/lib/:/root/anaconda3/pkgs/cudnn-7.6.5-cuda10.0_0/lib/:$LD_LIBRARY_PATH \
ffmpeg -i "/root/Desktop/works/pyjom/samples/video/LiEIfnsvn.mp4" \
-y -vf \
"sr=dnn_backend=tensorflow:model=./sr/espcn.pb,yaepblur" \
supertest.mp4

use standard scale method:

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ffmpeg -y -i "/root/Desktop/works/pyjom/tests/random_giphy_gifs/samoyed.gif"\
-vf "minterpolate,scale=w=iw*2:h=ih*2:flags=lanczos,hqdn3d" \
-r 60 ffmpeg_samoyed.mp4

options:

‘fast_bilinear’

Select fast bilinear scaling algorithm.

‘bilinear’

Select bilinear scaling algorithm.

‘bicubic’

Select bicubic scaling algorithm.

‘experimental’

Select experimental scaling algorithm.

‘neighbor’

Select nearest neighbor rescaling algorithm.

‘area’

Select averaging area rescaling algorithm.

‘bicublin’

Select bicubic scaling algorithm for the luma component, bilinear for chroma components.

‘gauss’

Select Gaussian rescaling algorithm.

‘sinc’

Select sinc rescaling algorithm.

‘lanczos’

Select Lanczos rescaling algorithm. The default width (alpha) is 3 and can be changed by setting param0.

‘spline’

Select natural bicubic spline rescaling algorithm.

‘print_info’

Enable printing/debug logging.

‘accurate_rnd’

Enable accurate rounding.

‘full_chroma_int’

Enable full chroma interpolation.

‘full_chroma_inp’

Select full chroma input.

‘bitexact’

Enable bitexact output.

https://github.com/slowmoVideo/slowmoVideo

it uses gpu and optical flow to do frame interpolation.

able to do instance segmentation if the optical flow boundary is clear and continuous.

build opencv with opencv_contrib and -DWITH_CUDA=ON to enable cudaoptflow.