Question

主程序

# In[10]:
cap = FileVideoStream('london_piccadilly_circus.mp4').start()
fps = FPS().start()

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while True:
      ### for webcam ###
      image_np = cap.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)
      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.
      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.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      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})
      print("Finish Actual Detection")

      # 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)
      print("Finish Visualization")

      ### for webcam ###
      cv2.imshow('object detection', cv2.resize(image_np, (300,300)))
      if cv2.waitKey(1) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        cap.stop()
        break

      fps.update()

fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

用于多线程的FileVideoStream程序

class FileVideoStream:
    def __init__(self, path, queueSize=128):
        # initialize the file video stream along with the boolean
        # used to indicate if the thread should be stopped or not
        self.stream = cv2.VideoCapture(path)
        self.stopped = False

        # initialize the queue used to store frames read from
        # the video file
        self.Q = Queue(maxsize=queueSize)
        print("FileVideoStream Initialized.")

    def start(self):
        # start a thread to read frames from the file video stream
        t = Thread(target=self.update, args=())
        t.daemon = True
        t.start()
        return self

    def update(self):
        # keep looping infinitely
        while True:
            # if the thread indicator variable is set, stop the
            # thread
            if self.stopped:
                return

            # otherwise, ensure the queue has room in it
            if not self.Q.full():
                # read the next frame from the file
                (grabbed, frame) = self.stream.read()

                # if the `grabbed` boolean is `False`, then we have
                # reached the end of the video file
                if not grabbed:
                    self.stop()
                    return

                # add the frame to the queue
                self.Q.put(frame)
            print("Queue size {}".format(self.Q.qsize()))
            print("Pass")

    def read(self):
        # return next frame in the queue
        return self.Q.get()

    def more(self):
        # return True if there are still frames in the queue
        return self.Q.qsize() > 0

    def stop(self):
        # indicate that the thread should be stopped
        self.stopped = True

我正在尝试增加用于对象检测的FPS。我正在使用Tensorflow API进行检测。我也遵循了本指南（https://www.pyimagesearch.com/2017/02/06/faster-video-file-fps-with-cv2-videocapture-and-opencv/），它使用线程来尝试增加FPS。主程序和filevideostream代码如上所示。我得到的FPS小于1.从我观察到的情况来看，与提供帧的filevideostream线程相比，可视化和实际检测是如此之慢。这导致滞后。如何增加FPS以便可以更平滑地显示帧（实时）？

非常感谢

Answer 1

您可以尝试一些方法：

如果您的计算机上有兼容的图形卡，请确保在启用GPU的情况下运行TensorFlow。
如果你在CPU上运行，enable Intel's MKL用于x86加速。

您可以在https://www.tensorflow.org/performance/performance_guide

找到更多有关改善效果的一般建议

增加对象检测API的FPS

1 个答案: