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  • Websites made ​​with MediaSPIP

    2 mai 2011, par

    This page lists some websites based on MediaSPIP.

  • MediaSPIP v0.2

    21 juin 2013, par

    MediaSPIP 0.2 est la première version de MediaSPIP stable.
    Sa date de sortie officielle est le 21 juin 2013 et est annoncée ici.
    Le fichier zip ici présent contient uniquement les sources de MediaSPIP en version standalone.
    Comme pour la version précédente, il est nécessaire d’installer manuellement l’ensemble des dépendances logicielles sur le serveur.
    Si vous souhaitez utiliser cette archive pour une installation en mode ferme, il vous faudra également procéder à d’autres modifications (...)

  • Creating farms of unique websites

    13 avril 2011, par

    MediaSPIP platforms can be installed as a farm, with a single "core" hosted on a dedicated server and used by multiple websites.
    This allows (among other things) : implementation costs to be shared between several different projects / individuals rapid deployment of multiple unique sites creation of groups of like-minded sites, making it possible to browse media in a more controlled and selective environment than the major "open" (...)

Sur d’autres sites (10178)

  • dnn/native : add native support for divide

    11 avril 2020, par Guo, Yejun
    dnn/native : add native support for divide

    it can be tested with model file generated with below python script :
    
import tensorflow as tf
    
import numpy as np
    
import imageio
    

    in_img = imageio.imread('input.jpg')
    
in_img = in_img.astype(np.float32)/255.0
    
in_data = in_img[np.newaxis, :]
    

    x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='dnn_in')
    
z1 = 2 / x
    
z2 = 1 / z1
    
z3 = z2 / 0.25 + 0.3
    
z4 = z3 - x * 1.5 - 0.3
    
y = tf.identity(z4, name='dnn_out')
    

    sess=tf.Session()
    
sess.run(tf.global_variables_initializer())
    

    graph_def = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ['dnn_out'])
    
tf.train.write_graph(graph_def, '.', 'image_process.pb', as_text=False)
    

    print("image_process.pb generated, please use \
    
path_to_ffmpeg/tools/python/convert.py to generate image_process.model\n")
    

    output = sess.run(y, feed_dict=x : in_data)
    
imageio.imsave("out.jpg", np.squeeze(output))
    

    Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathbinary.c
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathbinary.h
    • [DH] tools/python/convert_from_tensorflow.py
    • [DH] tools/python/convert_header.py

  • How do I set Individual Input Volumes using a FFMPEG complex filter with Amix

    18 août 2022, par James Parker

    How do I set the individual volume levels for 4 individual inputs using FFMPEG and a complex filter ?

    &#xA;

    This is the filter that I currently have.

    &#xA;

    xstack=inputs=${inputs.length}:layout=0_0|w0_0|0_h0|w0_h0;amix=inputs=${inputs.length}

    &#xA;

  • Individual video watermark for user

    15 février 2017, par zona

    Need to set individual watermark for each user while he downloads video.
    Watermark should be placed for 3-5 seconds in the same place and starts from N second of video. How I can do it without full video reconvert and as fast as it can be.

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