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  • Des sites réalisés avec MediaSPIP

    2 mai 2011, par

    Cette page présente quelques-uns des sites fonctionnant sous MediaSPIP.
    Vous pouvez bien entendu ajouter le votre grâce au formulaire en bas de page.

  • Support audio et vidéo HTML5

    10 avril 2011

    MediaSPIP utilise les balises HTML5 video et audio pour la lecture de documents multimedia en profitant des dernières innovations du W3C supportées par les navigateurs modernes.
    Pour les navigateurs plus anciens, le lecteur flash Flowplayer est utilisé.
    Le lecteur HTML5 utilisé a été spécifiquement créé pour MediaSPIP : il est complètement modifiable graphiquement pour correspondre à un thème choisi.
    Ces technologies permettent de distribuer vidéo et son à la fois sur des ordinateurs conventionnels (...)

  • HTML5 audio and video support

    13 avril 2011, par

    MediaSPIP uses HTML5 video and audio tags to play multimedia files, taking advantage of the latest W3C innovations supported by modern browsers.
    The MediaSPIP player used has been created specifically for MediaSPIP and can be easily adapted to fit in with a specific theme.
    For older browsers the Flowplayer flash fallback is used.
    MediaSPIP allows for media playback on major mobile platforms with the above (...)

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  • dnn_backend_native_layer_mathunary : add acos support

    18 juin 2020, par Ting Fu
    dnn_backend_native_layer_mathunary : add acos support

    It can be tested with the model generated with below python script :
    

    import tensorflow as tf
    
import numpy as np
    
import imageio
    

    in_img = imageio.imread('input.jpeg')
    
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')
    
x1 = tf.acos(x)
    
x2 = tf.divide(x1, 3.1416/2) # pi/2
    
y = tf.identity(x2, 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 : Ting Fu <ting.fu@intel.com>
    
Signed-off-by : Guo Yejun <yejun.guo@intel.com>
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
    • [DH] tools/python/convert_from_tensorflow.py
    • [DH] tools/python/convert_header.py

  • dnn_backend_native_layer_mathunary : add asin support

    18 juin 2020, par Ting Fu
    dnn_backend_native_layer_mathunary : add asin support

    It can be tested with the model generated with below python script :
    

    import tensorflow as tf
    
import numpy as np
    
import imageio
    

    in_img = imageio.imread('input.jpeg')
    
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')
    
x1 = tf.asin(x)
    
x2 = tf.divide(x1, 3.1416/2) # pi/2
    
y = tf.identity(x2, 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 : Ting Fu <ting.fu@intel.com>
    
Signed-off-by : Guo Yejun <yejun.guo@intel.com>
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
    • [DH] tools/python/convert_from_tensorflow.py
    • [DH] tools/python/convert_header.py

  • dnn_backend_native_layer_mathunary : add atanh support

    29 juin 2020, par Ting Fu
    dnn_backend_native_layer_mathunary : add atanh support

    It can be tested with the model generated with below python script :
    

    import tensorflow as tf
    
import numpy as np
    
import imageio
    

    in_img = imageio.imread('input.jpeg')
    
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')
    

    please uncomment the part you want to test
    

    x_sinh_1 = tf.sinh(x)
    
x_out = tf.divide(x_sinh_1, 1.176) # sinh(1.0)
    

    x_cosh_1 = tf.cosh(x)
    
x_out = tf.divide(x_cosh_1, 1.55) # cosh(1.0)
    

    x_tanh_1 = tf.tanh(x)
    
x__out = tf.divide(x_tanh_1, 0.77) # tanh(1.0)
    

    x_asinh_1 = tf.asinh(x)
    
x_out = tf.divide(x_asinh_1, 0.89) # asinh(1.0/1.1)
    

    x_acosh_1 = tf.add(x, 1.1)
    
x_acosh_2 = tf.acosh(x_acosh_1) # accept (1, inf)
    
x_out = tf.divide(x_acosh_2, 1.4) # acosh(2.1)
    

    x_atanh_1 = tf.divide(x, 1.1)
    
x_atanh_2 = tf.atanh(x_atanh_1) # accept (-1, 1)
    
x_out = tf.divide(x_atanh_2, 1.55) # atanhh(1.0/1.1)
    

    y = tf.identity(x_out, name='dnn_out') #please only preserve the x_out you want to test
    

    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 : Ting Fu <ting.fu@intel.com>
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.c
    • [DH] libavfilter/dnn/dnn_backend_native_layer_mathunary.h
    • [DH] tools/python/convert_from_tensorflow.py
    • [DH] tools/python/convert_header.py

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