Использование многопоточности для вывода в Tensorflow с OpenCV

Я знаю, что использование многопоточности полезно для обучения DNN с помощью Tensorflow.

Но есть ли смысл использовать его для вывода? Например, если вы используете API обнаружения объектов Google для обнаружения объектов в реальном времени в видеопотоках? И если да, то как это реализовано?

Я создал репозиторий github (https://github.com/GustavZ/realtime_object_detection), который позволяет легко Обнаружение объекта времени, но меня не устраивает сгенерированный FPS, поэтому я подумал об использовании многопоточности для его ускорения.

Есть ли у кого-нибудь опыт с этим или может помочь мне реализовать его в моем коде?

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 21 12:01:40 2017
@author: GustavZ
"""
import numpy as np
import os
import six.moves.urllib as urllib
import tarfile
import tensorflow as tf
import cv2


# Protobuf Compilation (once necessary)
os.system('protoc object_detection/protos/*.proto --python_out=.')

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from stuff.helper import FPS2

# Define Video Input
# Must be OpenCV readable
# 0 = Default Camera
video_input = 0
width = 640
height = 480
fps_interval = 3

# Model preparation
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = 'models/' + MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
LABEL_MAP = 'mscoco_label_map.pbtxt'
PATH_TO_LABELS = 'object_detection/data/' + LABEL_MAP
NUM_CLASSES = 90

# Download Model    
if not os.path.isfile(PATH_TO_CKPT):
    print('Model not found. Downloading it now.')
    opener = urllib.request.URLopener()
    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
    tar_file = tarfile.open(MODEL_FILE)
    for file in tar_file.getmembers():
      file_name = os.path.basename(file.name)
      if 'frozen_inference_graph.pb' in file_name:
        tar_file.extract(file, os.getcwd())
    os.remove('../' + MODEL_FILE)
else:
    print('Model found. Proceed.')

# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

# Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

# Start Video Stream
video_stream = cv2.VideoCapture(video_input)
video_stream.set(cv2.CAP_PROP_FRAME_WIDTH, width)
video_stream.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

# Detection
print ("Press 'q' to Exit")
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess: # config=tf.ConfigProto(log_device_placement=True)
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    # fps calculation
    fps = FPS2(fps_interval).start()
    while video_stream.isOpened():
      ret_val,image_np = video_stream.read()
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      cv2.imshow('object_detection', image_np)
      # Exit Option
      if cv2.waitKey(1) & 0xFF == ord('q'):
          break
      fps.update()

# End everything
video_stream.release()     
cv2.destroyAllWindows()
fps.stop()
print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed()))
print('[INFO] approx. FPS: {:.2f}'.format(fps.fps()))