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  • Les statuts des instances de mutualisation

    13 mars 2010, par

    Pour des raisons de compatibilité générale du plugin de gestion de mutualisations avec les fonctions originales de SPIP, les statuts des instances sont les mêmes que pour tout autre objets (articles...), seuls leurs noms dans l’interface change quelque peu.
    Les différents statuts possibles sont : prepa (demandé) qui correspond à une instance demandée par un utilisateur. Si le site a déjà été créé par le passé, il est passé en mode désactivé. publie (validé) qui correspond à une instance validée par un (...)

  • Support de tous types de médias

    10 avril 2011

    Contrairement à beaucoup de logiciels et autres plate-formes modernes de partage de documents, MediaSPIP a l’ambition de gérer un maximum de formats de documents différents qu’ils soient de type : images (png, gif, jpg, bmp et autres...) ; audio (MP3, Ogg, Wav et autres...) ; vidéo (Avi, MP4, Ogv, mpg, mov, wmv et autres...) ; contenu textuel, code ou autres (open office, microsoft office (tableur, présentation), web (html, css), LaTeX, Google Earth) (...)

  • Supporting all media types

    13 avril 2011, par

    Unlike most software and media-sharing platforms, MediaSPIP aims to manage as many different media types as possible. The following are just a few examples from an ever-expanding list of supported formats : images : png, gif, jpg, bmp and more audio : MP3, Ogg, Wav and more video : AVI, MP4, OGV, mpg, mov, wmv and more text, code and other data : OpenOffice, Microsoft Office (Word, PowerPoint, Excel), web (html, CSS), LaTeX, Google Earth and (...)

Sur d’autres sites (8668)

  • three ways to achieve an H264 file

    18 mars 2016, par Kindermann

    here I have three ways to get an H264 file, like all forensic scientists, I am very curious about the differences between them :

    1.

    ffmpeg -i video.mp4 video.h264

    2.

    ffmpeg -i video.mp4 -vcodec copy -an -f h264 video.h264

    3. Using the example "demuxing_decoding.c" provided on the ffmpeg official website :
    http://ffmpeg.org/doxygen/trunk/demuxing_decoding_8c-example.html

    Obviously, the first one does the transformation, and the second one does the demuxing. They render different H264 files which however have similar file sizes(in my case, it’s about say 24 MB). Suprisingly, the third one, which is also supposed to do the demuxing job, renders an H264 file with 8.4 GB ! Why ?

    What I wondered is really, how the interiors of these three methods work ?(The third one is already in source code, therefore it’s quite easy to have an insight) What about the first two commands ? What APIs are called when executing these two commands and how those APIs are called(namely, in what kind of sequences they are called) and things like that.
    One thing that is also important to me is, i have no idea how I can trace the execution routines of ffmpeg command lines. I want to see what’s going on behind ffmpeg commands in source code version. Is it possible ?

    I appreciate any comment.

  • Process GIF using FFmpeg libraries - can't find parser at av_parser_init

    24 novembre 2018, par natario

    I am playing with ffmpeg libs, namely libswscale and libavcodec for now. My goal is resize GIF files. From my ridiculous understanding, I think I need to

    • decode the GIF and get an AVFrame
    • process the frame with libswscale
    • encode again into GIF

    But I am stuck at step 1. Based on official sample at https://github.com/FFmpeg/FFmpeg/blob/master/doc/examples/decode_video.c , I need to get a parser :

    codec = avcodec_find_decoder(AV_CODEC_ID_GIF);
    parser = av_parser_init(codec->id);

    But no parser is found. I am not touching parser in my configure call, so I take all :

    Enabled parsers:
    aac                       cavsvideo                 dvbsub                    h263                      mpegvideo                 sipr                      xma
    aac_latm                  cook                      dvd_nav                   h264                      opus                      tak
    ac3                       dca                       dvdsub                    hevc                      png                       vc1
    adx                       dirac                     flac                      mjpeg                     pnm                       vorbis
    av1                       dnxhd                     g729                      mlp                       rv30                      vp3
    avs2                      dpx                       gsm                       mpeg4video                rv40                      vp8
    bmp                       dvaudio                   h261                      mpegaudio                 sbc                       vp9

    What am I doing wrong ? If this is the wrong approach, what is the correct one ?

  • libavfi/dnn : add LibTorch as one of DNN backend

    15 mars 2024, par Wenbin Chen
    libavfi/dnn : add LibTorch as one of DNN backend
    

    PyTorch is an open source machine learning framework that accelerates
    the path from research prototyping to production deployment. Official
    website : https://pytorch.org/. We call the C++ library of PyTorch as
    LibTorch, the same below.

    To build FFmpeg with LibTorch, please take following steps as
    reference :
    1. download LibTorch C++ library in
    https://pytorch.org/get-started/locally/,
    please select C++/Java for language, and other options as your need.
    Please download cxx11 ABI version :
    (libtorch-cxx11-abi-shared-with-deps-*.zip).
    2. unzip the file to your own dir, with command
    unzip libtorch-shared-with-deps-latest.zip -d your_dir
    3. export libtorch_root/libtorch/include and
    libtorch_root/libtorch/include/torch/csrc/api/include to $PATH
    export libtorch_root/libtorch/lib/ to $LD_LIBRARY_PATH
    4. config FFmpeg with ../configure —enable-libtorch \
    —extra-cflag=-I/libtorch_root/libtorch/include \
    —extra-cflag=-I/libtorch_root/libtorch/include/torch/csrc/api/include \
    —extra-ldflags=-L/libtorch_root/libtorch/lib/
    5. make

    To run FFmpeg DNN inference with LibTorch backend :
    ./ffmpeg -i input.jpg -vf \
    dnn_processing=dnn_backend=torch:model=LibTorch_model.pt -y output.jpg

    The LibTorch_model.pt can be generated by Python with torch.jit.script()
    api. https://pytorch.org/tutorials/advanced/cpp_export.html. This is
    pytorch official guide about how to convert and load torchscript model.
    Please note, torch.jit.trace() is not recommanded, since it does
    not support ambiguous input size.

    Signed-off-by : Ting Fu <ting.fu@intel.com>
    Signed-off-by : Wenbin Chen <wenbin.chen@intel.com>
    Reviewed-by : Guo Yejun <yejun.guo@intel.com>

    • [DH] configure
    • [DH] libavfilter/dnn/Makefile
    • [DH] libavfilter/dnn/dnn_backend_torch.cpp
    • [DH] libavfilter/dnn/dnn_interface.c
    • [DH] libavfilter/dnn_filter_common.c
    • [DH] libavfilter/dnn_interface.h
    • [DH] libavfilter/vf_dnn_processing.c