张量流中边界框的坐标

Question

我想要张量流模型中预测边界框的坐标。我正在使用here中的对象检测脚本。在关于stackoverflow的一些答案之后，我将检测的最后一块修改为

for image_path in TEST_IMAGE_PATHS:
  image = Image.open(image_path)
  # 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 = load_image_into_numpy_array(image)
  # 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.
  output_dict = run_inference_for_single_image(image_np, detection_graph)
  # Visualization of the results of a detection.
  width, height = image.size
  print(width,height)
  ymin = output_dict['detection_boxes'][5][0]*height
  xmin = output_dict['detection_boxes'][5][1]*width
  ymax = output_dict['detection_boxes'][5][2]*height
  xmax = output_dict['detection_boxes'][5][3]*width
  #print(output_dict['detection_boxes'][0])
  print (xmin,ymin)
  print (xmax,ymax)

但是output_dict ['detection_boxes']中有100个元组。即使对于无法预测的图像，也存在100个元组。

我想要的是单个图像所有边界框的坐标。

我想要张量流模型中预测边界框的坐标。我正在从这里使用对象检测脚本。在对stackoverflow做出一些回答之后，我修改了...

Answer 1

expand_dims行之后，您可以添加这些代码。 filtered_boxes变量将给出其预测值大于0.5的边界框。

Answer 2

如果检查正在使用的模型的pipeline.config文件，则可以看到在某些地方最大框数设置为100。例如，在演示笔记本中的型号config file of ssd_mobilenet_v1中，您可以在[]下看到它

post_processing {
  batch_non_max_suppression {
    ...
    max_detections_per_class: 100
    max_total_detections: 100
  }
}

Answer 3

for image_path in TEST_IMAGE_PATHS:
  image_np = cv2.imread(image_path)
  # 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.
  output_dict = run_inference_for_single_image(image_np, detection_graph)
  # 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'),
      use_normalized_coordinates=True,
      line_thickness=8)

#if using cv2 to load image
(im_width, im_height) = image_np.shape[:2]

ymin = output_dict['detection_boxes'][0][0]*im_height
xmin = output_dict['detection_boxes'][0][1]*im_width
ymax = output_dict['detection_boxes'][0][2]*im_height
xmax = output_dict['detection_boxes'][0][3]*im_width