Question

我是python和Tensorflow的新手。我正在尝试从Tensorflow Object Detection API运行object_detection_tutorial文件，但是当检测到物体时，我无法找到可以获取边界框坐标的位置。

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 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)
  plt.figure(figsize=IMAGE_SIZE)
  plt.imshow(image_np)

我尝试打印output_dict ['detection_boxes']，但我不确定数字是什么意思。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，但我没有一个名为box的变量。我怎样才能获得坐标？谢谢！

Answer 1

我尝试打印output_dict [＆＃39; detection_boxes＆＃39;]但我不确定是什么数字意味着

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

请注意，您正在传递use_normalized_coordinates=True。如果您跟踪函数调用，您会看到数字[ 0.56213236, 0.2780568 , 0.91445708, 0.69120586]等是图像坐标所在的值[ymin, xmin, ymax, xmax]：

(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)

Answer 2

我有完全一样的故事。当图像上仅显示一个框时，得到的数组包含大约一百个框（output_dict['detection_boxes']。深入研究绘制矩形的代码能够将其提取并用于我的inference.py：

#so detection has happened and you've got output_dict as a
# result of your inference

# then assume you've got this in your inference.py in order to draw rectangles
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)

# This is the way I'm getting my coordinates
boxes = output_dict['detection_boxes']
# get all boxes from an array
max_boxes_to_draw = boxes.shape[0]
# get scores to get a threshold
scores = output_dict['detection_scores']
# this is set as a default but feel free to adjust it to your needs
min_score_thresh=.5
# iterate over all objects found
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
    # 
    if scores is None or scores[i] > min_score_thresh:
        # boxes[i] is the box which will be drawn
        class_name = category_index[output_dict['detection_classes'][i]]['name']
        print ("This box is gonna get used", boxes[i], output_dict['detection_classes'][i])

Answer 3

以上答案对我不起作用，我不得不做一些更改。所以如果这没有帮助，也许试试这个。

# This is the way I'm getting my coordinates
boxes = detections['detection_boxes'].numpy()[0]
# get all boxes from an array
max_boxes_to_draw = boxes.shape[0]
# get scores to get a threshold
scores = detections['detection_scores'].numpy()[0]
# this is set as a default but feel free to adjust it to your needs
min_score_thresh=.5
# # iterate over all objects found
coordinates = []
for i in range(min(max_boxes_to_draw, boxes.shape[0])):
    if scores[i] > min_score_thresh:
        class_id = int(detections['detection_classes'].numpy()[0][i] + 1)
        coordinates.append({
            "box": boxes[i],
            "class_name": category_index[class_id]["name"],
            "score": scores[i]
        })


print(coordinates)

这里坐标列表中的每个项目（字典）都是一个要在图像上绘制的框，其中包含框坐标（标准化）、class_name 和分数。

获取TensorFlow对象检测API教程中的边界框坐标

3 个答案: