opencv - 如何在运行 Tensorflow 推理 session 之前批处理多个视频帧

Question

我做了一个项目，基本上使用带有 tensorflow 的 googles object detection api。

我所做的只是使用预训练模型进行推理:这意味着实时对象检测，其中输入是网络摄像头的视频流或使用 OpenCV 的类似内容。

现在我得到了相当不错的性能结果，但我想进一步提高 FPS。

因为我的经验是 Tensorflow 在推理时使用了我的整个内存，但 GPU 使用率根本没有达到最大值(NVIDIA GTX 1050 笔记本电脑大约 40%，NVIDIA Jetson Tx2 大约 6%)。

所以我的想法是通过增加每次 session 运行时输入的图像批量大小来增加 GPU 使用率。

所以我的问题是:在将输入视频流的多个帧提供给 sess.run() 之前，如何将它们一起批处理？

在我的 github 存储库中查看我的代码 object_detetection.py:( https://github.com/GustavZ/realtime_object_detection)。

如果您能提出一些提示或代码实现，我将不胜感激!

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, WebcamVideoStream

# INPUT PARAMS
# Must be OpenCV readable
# 0 = Default Camera
video_input = 0
visualize = True
max_frames = 300 #only used if visualize==False
width = 640
height = 480
fps_interval = 3
bbox_thickness = 8

# 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 = WebcamVideoStream(video_input,width,height).start()
cur_frames = 0
# Detection
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # 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()
    print ("Press 'q' to Exit")
    while video_stream.isActive():
      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=bbox_thickness)
      if visualize:
          cv2.imshow('object_detection', image_np)
          # Exit Option
          if cv2.waitKey(1) & 0xFF == ord('q'):
              break
      else:
          cur_frames += 1
          if cur_frames >= max_frames:
              break
      # fps calculation
      fps.update()

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

Answer 1

好吧，我只是收集 batch_size 帧并提供它们:

batch_size = 5
while video_stream.isActive():
  image_np_list = []
  for _ in range(batch_size):
      image_np_list.append(video_stream.read())
      fps.update()
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
  image_np_expanded = np.asarray(image_np_list)
  # 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.
  for i in range(batch_size):
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np_expanded[i],
          boxes[i],
          classes[i].astype(np.int32),
          scores[i],
          category_index,
          use_normalized_coordinates=True,
          line_thickness=bbox_thickness)
          if visualize:
              cv2.imshow('object_detection', image_np_expanded[i])
              # Exit Option
              if cv2.waitKey(1) & 0xFF == ord('q'):
                  break

当然，如果您正在读取检测结果，您必须在此之后进行相关更改，因为它们现在将具有 batch_size 行。

不过要小心:在 tensorflow 1.4 之前(我认为)，对象检测 API only supports batch size of 1在 image_tensor 中，所以这将不起作用，除非您升级您的 tensorflow。

另请注意，您生成的 FPS 将是平均值，但同一批处理中的帧实际上在时间上比不同批处理之间更接近(因为您仍然需要等待 sess.run( ) 结束)。尽管两个连续帧之间的最长时间应该增加，但平均值仍应明显优于您当前的 FPS。

如果您希望帧之间的间隔大致相同，我想您需要更复杂的工具，例如多线程和队列:一个线程将从流中读取图像并将它们存储在队列中，另一个一种是从队列中取出它们并异步调用它们的 sess.run()；它还可以告诉第一个线程根据其自身的计算能力加快或减慢速度。这实现起来比较棘手。