我是 Python 和 Tensorflow 的新手。我正在尝试从 Tensorflow Object Detection API 运行对象检测教程文件, 但是当检测到对象时，我找不到在哪里可以获得边界框的坐标。

相关代码:

 # The following processing is only for single image
 detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
 detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])

我假设绘制边界框的地方是这样的:

 # Visualization of the results of 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)
 plt.figure(figsize=IMAGE_SIZE)
 plt.imshow(image_np)

我尝试打印 output_dict['detection_boxes'] 但我不确定数字的含义。有很多。

array([[ 0.56213236,  0.2780568 ,  0.91445708,  0.69120586],
       [ 0.56261235,  0.86368728,  0.59286624,  0.8893863 ],
       [ 0.57073039,  0.87096912,  0.61292225,  0.90354401],
       [ 0.51422435,  0.78449738,  0.53994244,  0.79437423],
......

       [ 0.32784131,  0.5461576 ,  0.36972913,  0.56903434],
       [ 0.03005961,  0.02714229,  0.47211722,  0.44683522],
       [ 0.43143299, 0.09211366,  0.58121657,  0.3509962 ]], dtype=float32)

我找到了类似问题的答案，但我没有像他们那样有一个名为 boxes 的变量。如何获取坐标？

最佳答案

I tried printing output_dict['detection_boxes'] but I am not sure what the numbers mean

您可以自己查看代码。 visualize_boxes_and_labels_on_image_array 定义 here .

请注意，您正在传递 use_normalized_coordinates=True。如果跟踪函数调用，您将看到您的数字 [ 0.56213236, 0.2780568 , 0.91445708, 0.69120586] 等是值 [ymin, xmin, ymax, xmax] 其中图片坐标:

(left, right, top, bottom) = (xmin * im_width, xmax * im_width, 
                              ymin * im_height, ymax * im_height)

由函数计算:

def draw_bounding_box_on_image(image,
                           ymin,
                           xmin,
                           ymax,
                           xmax,
                           color='red',
                           thickness=4,
                           display_str_list=(),
                           use_normalized_coordinates=True):
  """Adds a bounding box to an image.
  Bounding box coordinates can be specified in either absolute (pixel) or
  normalized coordinates by setting the use_normalized_coordinates argument.
  Each string in display_str_list is displayed on a separate line above the
  bounding box in black text on a rectangle filled with the input 'color'.
  If the top of the bounding box extends to the edge of the image, the strings
  are displayed below the bounding box.
  Args:
    image: a PIL.Image object.
    ymin: ymin of bounding box.
    xmin: xmin of bounding box.
    ymax: ymax of bounding box.
    xmax: xmax of bounding box.
    color: color to draw bounding box. Default is red.
    thickness: line thickness. Default value is 4.
    display_str_list: list of strings to display in box
                      (each to be shown on its own line).
    use_normalized_coordinates: If True (default), treat coordinates
      ymin, xmin, ymax, xmax as relative to the image.  Otherwise treat
      coordinates as absolute.
  """
  draw = ImageDraw.Draw(image)
  im_width, im_height = image.size
  if use_normalized_coordinates:
    (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                  ymin * im_height, ymax * im_height)

关于python - TensorFlow对象检测API教程中获取边界框坐标，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/48915003/