当我尝试运行此代码时：

  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  image_np = np.array(Image.open(image_path))
  # actual detection.
  output_dict = run_inference_for_single_image(model,image_np)
  # Visualization of the results of a detection.
  vis_util.visualize_boxes_and_labels_on_image_array(
      image_np,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'],category_index,instance_masks=output_dict.get('detection_masks_reframed',None),use_normalized_coordinates=True,line_thickness=8)

  display(Image.fromarray(image_np))
for image_path in TEST_IMAGE_PATHS:
  show_inference(detection_model,image_path)

由于某些原因，它始终显示“内核似乎已死亡。它将自动重新启动。”

以前，我能够运行此代码。.但现在它继续显示内核已死亡

我尝试使用conda update mkl更新环境

从（源或二进制）安装的TensorFlow： 使用pip安装（pip install tensorflow-gpu） TF版本：2.0 繁殖方法： 运行object_detection_tutorial.ipynb文件