Sur d’autres sites (5385)

• dnn/native : add native support for minimum

  26 avril 2020, par Guo, Yejun

  dnn/native : add native support for minimum
  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')
  
  x1 = tf.minimum(0.7, x)
  
  x2 = tf.maximum(x1, 0.4)
  
  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 : 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

• 【carrierwave+ffmpeg】can't upload the video with sound

  10 avril 2020, par ken akita

  I want to make "Fake Instagram" Rails application.
then I made the uploader and I can upload the video(mp4), but no sound.
I can listen to the sound on my desktop but can't on my application, like this image, speakers not working.
enter image description here

  



  Code is here. I guess something wrong with image_uploader.rb or show.html.erb.
Note that first I made the ImageUploader to upload image, then I want to add video posting system to upload video. Both uploading done, but video without sound.
Really thank you for your advise !

  



  *image_uploader.rb
require 'streamio-ffmpeg'

class ImageUploader < CarrierWave::Uploader::Base
  include CarrierWave::MiniMagick
  process resize_to_fit: [230, 183]

  storage :file

  def store_dir
    "uploads/#{model.class.to_s.underscore}/#{mounted_as}/#{model.id}"
  end

  version :thumb do
    process resize_to_fit: [230, 183]
  end
  version :screenshot do
    process :screenshot
    def full_filename (for_file = model.logo.file)
      "screenshot.jpg"
    end
  end

  def screenshot
    tmpfile = File.join(File.dirname(current_path), "tmpfile")

    File.rename(current_path, tmpfile)

    movie = FFMPEG::Movie.new(tmpfile)
    movie.screenshot(current_path + ".jpg", {resolution: '230x183' }, preserve_aspect_ratio: :width)
    File.rename(current_path + ".jpg", current_path)

    File.delete(tmpfile)
  end

  def extension_whitelist
    %w(jpg jpeg gif png mp4)
  end
end


  



  *show.html.erb&#xA;<h1>Stroll</h1>&#xA;<p>&lt;%= notice %></p>&#xA;<p></p>&#xA;&lt;% if @stroll[:image] == nil %>&#xA;  &lt;%= image_tag"03summer_ver8-l.jpg", width:"230", height:"183" %>&#xA;&lt;% elsif @stroll.image.file.content_type.include?(&#x27;video/&#x27;) %>&#xA;  &lt;%= link_to @stroll.image_url.to_s do %>&#xA;  &lt;%= image_tag(@stroll.image_url(:screenshot).to_s, id: "image", :alt => "screenshot") %>&#xA;  &lt;% end %>&#xA;&lt;% else %>&#xA;  &lt;%= image_tag(@stroll.image.url) if @stroll.image &amp;&amp; @stroll.image.url %>&#xA;&lt;% end %>&#xA;<p>user &lt;%= @stroll.user.name %></p>&#xA;<p>『&lt;%= @stroll.title %>』</p>&#xA;<p>content: &lt;%= @stroll.content %></p>&#xA;<p>tag: &lt;%= @stroll.tag %></p>&#xA;<p>comment: </p>&#xA;<div>&#xA;  &lt;%= render partial: &#x27;comments/index&#x27;, locals: { comments: @comments, stroll: @stroll } %>&#xA;</div>&#xA;&lt;% if user_signed_in? &amp;&amp; @stroll.user_id != current_user.id %>&#xA;  &lt;%= render partial: &#x27;comments/form&#x27;, locals: { comment: @comment, stroll: @stroll } %>&#xA;&lt;% end %>&#xA;&lt;% if current_user %>&#xA;  &lt;% unless @stroll.user_id == current_user.id %>&#xA;    &lt;% if @favorite.present? %>&#xA;      &lt;%= link_to &#x27;cancel&#x27;, favorite_path(id: @favorite.id), method: :delete, class: &#x27;btn btn-info&#x27; %>&#xA;      &lt;% else %>&#xA;      &lt;%= link_to &#x27;Like！&#x27;, favorites_path(stroll_id: @stroll.id), method: :post, class: &#x27;btn btn-warning&#x27; %>&#xA;    &lt;% end %>&#xA;  &lt;% end %>&#xA;&lt;% end %>&#xA;&amp;emsp;&#xA;&lt;%= link_to "Index", strolls_path %>&#xA;

  


• How to create a spectrogram image from an audio file in Python just like how FFMPEG does ?

  2 mai 2020, par hamandishe Mk

  My code :

  



  import matplotlib.pyplot as plt
from matplotlib.pyplot import specgram
import librosa
import librosa.display
import numpy as np
import io
from PIL import Image

samples, sample_rate = librosa.load('thabo.wav')
fig = plt.figure(figsize=[4, 4])
ax = fig.add_subplot(111)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.set_frame_on(False)
S = librosa.feature.melspectrogram(y=samples, sr=sample_rate)
librosa.display.specshow(librosa.power_to_db(S, ref=np.max))
buf = io.BytesIO()
plt.savefig(buf,  bbox_inches='tight',pad_inches=0)

# plt.close('all')
buf.seek(0)
im = Image.open(buf)
# im = Image.open(buf).convert('L')
im.show()
buf.close()


  



  Spectrogram produced

  



  

  



  Using FFMPEG

  



  ffmpeg -i thabo.wav -lavfi showspectrumpic=s=224x224:mode=separate:legend=disabled spectrogram.png

  



  Spectrogram produced

  



  

  



  Please help, i want a spectrogram that is exactly the same as the one produced by FFMPEG, for use with a speech recognition model exported from google's teachable machine.
Offline recognition