Sockets 为每个视频帧检索相同的预测结果时出现问题

我有以下代码，它将中间预测结果从客户端发送到服务器。首先，它在前3层上执行预测，然后从3层开始在服务器端执行预测，并在服务器端打印预测结果。我在共享的视频链接上使用此执行预测

Client.py

model=VGG19(weights='imagenet')
def split_model(model, index):
   layer_input_1 = Input(model.layers[0].input_shape[1:])
   x = layer_input_1
   for layer in model.layers[1:index]:
      x = layer(x)
   model1 = Model(inputs=layer_input_1, outputs=x)
   input_shape_2 = model.layers[index].get_input_shape_at(0)[1:]
   layer_input_2 = Input(shape=input_shape_2)
   x = layer_input_2
   for layer in model.layers[index:]:
     x = layer(x)
   model2 = Model(inputs=layer_input_2, outputs=x)
   return (model1, model2)

m1,m2=split_model(model,3)

context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.connect('tcp://localhost:5555')
videoFile = "D:/test.mp4"
camera = cv2.VideoCapture(videoFile)
while True:
  grabbed, frame = camera.read()
  try:
      frame = cv2.resize( frame, (224, 224) ).astype( "float32" )
  except cv2.error:
      break
 image= img_to_array(frame)
 image=image.reshape((1,image.shape[0],image.shape[1],image.shape[2]))
 image=preprocess_input(image)
 preds=m1.predict(image)
 socket.send_pyobj(preds)
socket.close()

Server.py

 model=VGG19(weights='imagenet')
 def split_keras_model(model, index):
  layer_input_1 = Input(model.layers[0].input_shape[1:])
  x = layer_input_1
  for layer in model.layers[1:index]:
     x = layer(x)
  model1 = Model(inputs=layer_input_1, outputs=x)
  input_shape_2 = model.layers[index].get_input_shape_at(0)[1:]
  layer_input_2 = Input(shape=input_shape_2)
  x = layer_input_2
  for layer in model.layers[index:]:
     x = layer(x)
  model2 = Model(inputs=layer_input_2, outputs=x)
  return (model1, model2)  
 m1,m2=split_keras_model(model,3)
 context = zmq.Context()
 footage_socket = context.socket(zmq.SUB)
 footage_socket.bind('tcp://*:5555')
 footage_socket.setsockopt_string(zmq.SUBSCRIBE, np.unicode(''))
 while True:
    frame = footage_socket.recv_pyobj()
    tmp = np.zeros( frame.shape )
    for i in range( 0, 1 ):
        tmp[i,:] = tmp[i, :]
        predictions_result = m2.predict( tmp )
        label = decode_predictions( predictions_result )
        print(label)
 footage_socket.close()

在运行上述代码时，我没有遇到任何错误，但输出不是我所期望的，我正在为每个帧检索相同的结果。下面是我正在检索的预测结果

[[('n03788365', 'mosquito_net', 0.067498334), ('n15075141', 'toilet_tissue', 0.02530327), ('n04209239', 'shower_curtain', 0.021614889), ('n03291819', 'envelope', 0.019242924), ('n04447861', 'toilet_seat', 0.014170088)]]
[[('n03788365', 'mosquito_net', 0.067498334), ('n15075141', 'toilet_tissue', 0.02530327), ('n04209239', 'shower_curtain', 0.021614889), ('n03291819', 'envelope', 0.019242924), ('n04447861', 'toilet_seat', 0.014170088)]]

非常感谢您的帮助