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• handling subprocess file output

  14 janvier 2019, par Sandip Kumar

  I am working on a project in which at some point user uploads a video and in backend i have to generate thumbnail for that video. I preferred ffmpeg for this purpose and the system is in django environment.
  This is my view function

  def upload(request):
  
      if request.method == 'POST':
  
          form = DocumentForm(request.POST, request.FILES)
  
          if form.is_valid():
  
              newdoc = Document(docfile=request.FILES['docfile'])
  
              filename = str(request.FILES['docfile'].name)
  
              newdoc.save()
  
              op = subprocess.call(['ffmpeg', '-i',"media/private/"+up, '-ss', '00:00:03.000', '-vframes', '1', 'abc.jpg']) // Generates thumbnail
  
              newdoc.thumbnail = op
  
              newdoc.save()
  
              return HttpResponseRedirect(reverse('list'))
  
      else:
  
          form = DocumentForm()

  The Document has the thumbnail field. I know that subprocess.call() returns returncode not any object so using op variable is useless. So my query is how do i save generated thumbnail in model.

• FPS goes down while performing object detection using TensorFlow on multiple threads

  14 mai 2020, par Apoorv Mishra

  I am trying to run object detection on multiple cameras. I am using SSD mobinet v2 frozen graph to perform object detection with TensorFlow and OpenCV. I had implemented threading to invoke the separate thread for separate camera. But doing so I'm getting low FPS with multiple video streams.

  



  Note : The model is working fine with single stream. Also when number of detected objects in different frames are low, I'm getting decent FPS.

  



  My threading logic is working fine. I guess I'm having issue with using the graph and session. Please let me know what am I doing wrong.

  



  with tf.device('/GPU:0'):
    with detection_graph.as_default():
        with tf.Session(config=config, graph=detection_graph) as sess:
            while True:
                # Read frame from camera
                raw_image = pipe.stdout.read(IMG_H*IMG_W*3)
                image =  np.fromstring(raw_image, dtype='uint8')     # convert read bytes to np
                image_np = image.reshape((IMG_H,IMG_W,3))
                img_copy = image_np[170:480, 100:480]
                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
                image_np_expanded = np.expand_dims(img_copy, axis=0)
                # Extract image tensor
                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
                # Extract detection boxes
                boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
                # Extract detection scores
                scores = detection_graph.get_tensor_by_name('detection_scores:0')
                # Extract detection classes
                classes = detection_graph.get_tensor_by_name('detection_classes:0')
                # Extract number of detections
                num_detections = detection_graph.get_tensor_by_name(
                        'num_detections:0')
                # Actual detection.
                (boxes, scores, classes, num_detections) = sess.run(
                        [boxes, scores, classes, num_detections],
                        feed_dict={image_tensor: image_np_expanded})
                # Visualization of the results of a detection.
                boxes = np.squeeze(boxes)
                scores = np.squeeze(scores)
                classes = np.squeeze(classes).astype(np.int32)

                box_to_display_str_map = collections.defaultdict(list)
                box_to_color_map = collections.defaultdict(str)

                for i in range(min(max_boxes_to_draw, boxes.shape[0])):
                    if scores is None or scores[i] > threshold:
                        box = tuple(boxes[i].tolist())
                        if classes[i] in six.viewkeys(category_index):
                            class_name = category_index[classes[i]]['name']
                        display_str = str(class_name)
                        display_str = '{}: {}%'.format(display_str, int(100 * scores[i]))
                        box_to_display_str_map[box].append(display_str)
                        box_to_color_map[box] = STANDARD_COLORS[
                                classes[i] % len(STANDARD_COLORS)]
                for box,color in box_to_color_map.items():
                    ymin, xmin, ymax, xmax = box
                    flag = jam_check(xmin, ymin, xmax, ymax, frame_counter)
                    draw_bounding_box_on_image_array(
                            img_copy,
                            ymin,
                            xmin,
                            ymax,
                            xmax,
                            color=color,
                            thickness=line_thickness,
                            display_str_list=box_to_display_str_map[box],
                            use_normalized_coordinates=use_normalized_coordinates)

                image_np[170:480, 100:480] = img_copy

                image_np = image_np[...,::-1]

                pipe.stdout.flush()

                yield cv2.imencode('.jpg', image_np, [int(cv2.IMWRITE_JPEG_QUALITY), 50])[1].tobytes()


  



  I've set the config as :

  



  config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction=0.4


  


• Unable to upload video from iphon using paperclip-ffmpeg in ruby on rails

  13 mai 2015, par sank

  when i upload video from iphon then I am getting below error.

  It works perfectly when I upload same video from desktop.

  Command :: PATH=/usr/bin/:$PATH; file -b --mime '/tmp/6da355e988ec841811d8803dfd5cf44c20150513-8103-b4lkam.MOV'
  
  [paperclip] Content Type Spoof: Filename IMG_2637.MOV (["video/quicktime"]), content type discovered from file command: inode/x-empty. See documentation to allow this combination.
  
  (0.6ms)  ROLLBACK
  
  Completed 400 Bad Request in 58ms (Views: 0.9ms | ActiveRecord: 5.0ms)

  I used gems "paperclip", " > 4.1" and ’paperclip-ffmpeg’

  and in video model i used

  validates_attachment_content_type :student_video, content_type: /\Avideo\/.*\Z/

  also tried out

  validates_attachment_content_type :student_video, :content_type => ['video/x-   
  
  #                                        msvideo', 'video/avi', 'video/quicktime', 'video/3gpp', 'video/x-ms-
  
  #                                        wmv', 'video/mp4', 'flv-application/octet-stream', 'video/x-
  
  #                                        flv', 'video/mpeg', 'video/mpeg4', 'video/x-la-asf', 'video/x-ms-asf']

  but i m getting same error.

  I do not know what was an error.

  please help me.

  thanks in advance.