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• Sequelize FFMPEG get video after upload on NodeJs

  30 juin 2020, par jjplack

  Hello After upload a video to db using sequelize, i would like to edit it using FFMPEG

  


  So to get the video is just point the model attribute to FFMPEG ?

  


  Because using the file path is not editing the video.

  


  For exemple :

  


  fastify.route({
    method: "POST",
    url: "/posts",
    preHandler: upload.single("video"),

    handler: async function(request, reply) {
      const { Post } = fastify.sequelize;

      const videoPath = "./public/uploads/";

 

     

      const post = await Post.create({
        video: request.file.path,
        title: request.body.title,
   
      });
      reply.code(201).send(post);


 

try {
   const process = new ffmpeg(post.video);
  process.then(function (video) {
    video.addCommand('-ss', '00:01:00')
    video.addCommand('-vframes', '1')
    video.save(videoPath, function (error, file) {
        if (!error)
          console.log('Video file: ' + file);
      });
  }, function (err) {
    console.log('Error: ' + err);
  });
} catch (e) {

  console.log(e.msg);

}
      
    }
  });


  


• Efficient way to stream a sequence of frames

  3 juin 2020, par DoriHp 0

  I'm facing with an issue : I implemented a device to detect stranger for my home, which includes inputs from some IP camera and use a tensorflow model to process frame got from them.

  



  Now I want to build a dashboard (use Flask or Django - python framework as backend) to streaming the processed frames I got from the system, and if possible, do some transform on them (such as stack multi frames into one, etc), and run the server so I can watch it from distances. Currently, I'm sending frame by frame as independent images but it costs so much bandwidth. I read how h264 encoder work and felt very exicted about it. Now, the question is, how can I use h264 or any encoder like it transfer my data and reduce the bandwidth ?

  


• 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