Recherche avancée

Médias (1)

Mot : - Tags -/école

Autres articles (64)

  • Publier sur MédiaSpip

    13 juin 2013

    Puis-je poster des contenus à partir d’une tablette Ipad ?
    Oui, si votre Médiaspip installé est à la version 0.2 ou supérieure. Contacter au besoin l’administrateur de votre MédiaSpip pour le savoir

  • Ajouter notes et légendes aux images

    7 février 2011, par

    Pour pouvoir ajouter notes et légendes aux images, la première étape est d’installer le plugin "Légendes".
    Une fois le plugin activé, vous pouvez le configurer dans l’espace de configuration afin de modifier les droits de création / modification et de suppression des notes. Par défaut seuls les administrateurs du site peuvent ajouter des notes aux images.
    Modification lors de l’ajout d’un média
    Lors de l’ajout d’un média de type "image" un nouveau bouton apparait au dessus de la prévisualisation (...)

  • MediaSPIP 0.1 Beta version

    25 avril 2011, par

    MediaSPIP 0.1 beta is the first version of MediaSPIP proclaimed as "usable".
    The zip file provided here only contains the sources of MediaSPIP in its standalone version.
    To get a working installation, you must manually install all-software dependencies on the server.
    If you want to use this archive for an installation in "farm mode", you will also need to proceed to other manual (...)

Sur d’autres sites (7887)

  • dnn_backend_native_layer_mathunary : add cos support

    6 juin 2020, par Ting Fu
    dnn_backend_native_layer_mathunary : add cos support

    It can be tested with the model generated with below python scripy
    

    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.multiply(x, 1.5)
    
x2 = tf.cos(x1)
    
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_conv2d.c:Add mutithread function

    6 septembre 2020, par Xu Jun
    dnn_backend_native_layer_conv2d.c:Add mutithread function

    Use pthread to multithread dnn_execute_layer_conv2d.
    
Can be tested with command "./ffmpeg_g -i input.png -vf \
    
format=yuvj420p,dnn_processing=dnn_backend=native:model= \
    
espcn.model:input=x:output=y:options=conv2d_threads=23 \
    
 -y sr_native.jpg -benchmark"
    

    before patch : utime=11.238s stime=0.005s rtime=11.248s
    
after patch :  utime=20.817s stime=0.047s rtime=1.051s
    
on my 3900X 12c24t @4.2GHz
    

    About the increase of utime, it's because that CPU HyperThreading
    
technology makes logical cores twice of physical cores while cpu's
    
counting performance improves less than double. And utime sums
    
all cpu's logical cores' runtime. As a result, using threads num
    
near cpu's logical core's number will double utime, while reduce
    
rtime less than half for HyperThreading CPUs.
    

    Signed-off-by : Xu Jun <xujunzz@sjtu.edu.cn>
    
Signed-off-by : Guo, Yejun <yejun.guo@intel.com>
    • [DH] libavfilter/dnn/dnn_backend_native_layer_conv2d.c
    • [DH] tests/dnn/dnn-layer-conv2d-test.c

  • dnn_backend_native_layer_mathunary : add sin support

    6 juin 2020, par Ting Fu
    dnn_backend_native_layer_mathunary : add sin support

    It can be tested with the model file generated with below python scripy :
    

    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.multiply(x, 3.14)
    
x2 = tf.sin(x1)
    
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

Navigation