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Médias (3)

Mot : - Tags -/spip

Autres articles (25)

  • Encoding and processing into web-friendly formats

    13 avril 2011, par

    MediaSPIP automatically converts uploaded files to internet-compatible formats.
    Video files are encoded in MP4, Ogv and WebM (supported by HTML5) and MP4 (supported by Flash).
    Audio files are encoded in MP3 and Ogg (supported by HTML5) and MP3 (supported by Flash).
    Where possible, text is analyzed in order to retrieve the data needed for search engine detection, and then exported as a series of image files.
    All uploaded files are stored online in their original format, so you can (...)

  • 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) (...)

  • List of compatible distributions

    26 avril 2011, par

    The table below is the list of Linux distributions compatible with the automated installation script of MediaSPIP. Distribution nameVersion nameVersion number Debian Squeeze 6.x.x Debian Weezy 7.x.x Debian Jessie 8.x.x Ubuntu The Precise Pangolin 12.04 LTS Ubuntu The Trusty Tahr 14.04
    If you want to help us improve this list, you can provide us access to a machine whose distribution is not mentioned above or send the necessary fixes to add (...)

Sur d’autres sites (6475)

  • lavfi/dnn_classify : add filter dnn_classify for classification based on detection...

    17 mars 2021, par Guo, Yejun
    lavfi/dnn_classify : add filter dnn_classify for classification based on detection bounding boxes

    classification is done on every detection bounding box in frame's side data,
    
which are the results of object detection (filter dnn_detect).
    

    Please refer to commit log of dnn_detect for the material for detection,
    
and see below for classification.
    

    - download material for classifcation :
    
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.bin
    
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.xml
    
wget https://github.com/guoyejun/ffmpeg_dnn/raw/main/models/openvino/2021.1/emotions-recognition-retail-0003.label
    

    - run command as :
    
./ffmpeg -i cici.jpg -vf dnn_detect=dnn_backend=openvino:model=face-detection-adas-0001.xml:input=data:output=detection_out:confidence=0.6:labels=face-detection-adas-0001.label,dnn_classify=dnn_backend=openvino:model=emotions-recognition-retail-0003.xml:input=data:output=prob_emotion:confidence=0.3:labels=emotions-recognition-retail-0003.label:target=face,showinfo -f null -
    

    We'll see the detect&classify result as below :
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0]   side data - detection bounding boxes :
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0] source : face-detection-adas-0001.xml, emotions-recognition-retail-0003.xml
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index : 0,  region : (1005, 813) -> (1086, 905), label : face, confidence : 10000/10000.
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0]            classify :  label : happy, confidence : 6757/10000.
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0] index : 1,  region : (888, 839) -> (967, 926), label : face, confidence : 6917/10000.
    
[Parsed_showinfo_2 @ 0x55b7d25e77c0]            classify :  label : anger, confidence : 4320/10000.
    

    Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
    • [DH] configure
    • [DH] doc/filters.texi
    • [DH] libavfilter/Makefile
    • [DH] libavfilter/allfilters.c
    • [DH] libavfilter/vf_dnn_classify.c

  • fftools/ffmpeg_opt : Document VAAPI -device usage for DirectX Adapter

    14 avril 2023, par Sil Vilerino
    fftools/ffmpeg_opt : Document VAAPI -device usage for DirectX Adapter

    Initial review at https://github.com/intel-media-ci/ffmpeg/pull/619/
    

    Signed-off-by : Sil Vilerino <sivileri@microsoft.com>
    
Reviewed-by : Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
    
Reviewed-by : Wu, Tong1 <tong1.wu@intel.com>
    • [DH] doc/ffmpeg.texi
    • [DH] fftools/ffmpeg_opt.c

  • avconv / ffmpeg webcam capture while using minimum CPU processing

    10 septembre 2015, par user3585723

    I have a question about avconv (or ffmpeg) usage.

    My goal is to capture video from a webcam and saving it to a file.
    Also, I don’t want to use too much CPU processing. (I don’t want avconv to scale or re-encode the stream)

    So, I was thinking to use the compressed mjpeg video stream from the webcam and directly saving it to a file.

    My webcam is a Microsoft LifeCam HD 3000 and its capabilities are :

    ffmpeg -f v4l2 -list_formats all -i /dev/video0
    

    
Raw: yuyv422 : YUV 4:2:2 (YUYV) : 640x480 1280x720 960x544 800x448 640x360 424x240 352x288 320x240 800x600 176x144 160x120 1280x800
    

    
Compressed: mjpeg : MJPEG : 640x480 1280x720 960x544 800x448 640x360 800x600 416x240 352x288 176x144 320x240 160x120

    What would be the avconv command to save the Compressed stream directly without having avconv doing scaling or re-encoding.

    For now, I am using this command :

    avconv -f video4linux2 -r 30 -s 320x240 -i /dev/video0 test.avi

    I’m not sure that this command is CPU efficient since I don’t tell anywhere to use the mjpeg Compressed capability of the webcam.

    Is avconv taking care of the configuration of the webcam setting before starting to record the file ? Is it always working of raw stream and doing scaling and enconding on the raw stream ?

    Thanks for your answer

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