TensorFlow对象检测API教程中获取边界框坐标

Posted 2023-02-23

【中文标题】TensorFlow对象检测API教程中获取边界框坐标

【英文标题】：Get the bounding box coordinates in the TensorFlow object detection API tutorial

【发布时间】：2018-08-01 13:42:03

【问题描述】：

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']，但我不确定这些数字的含义。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 box 的变量。如何获取坐标？

【参考方案1】：

我尝试打印 output_dict['detection_boxes'] 但我不确定是什么 数字的意思

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

请注意，您传递的是use_normalized_coordinates=True。如果您跟踪函数调用，您将看到您的数字[ 0.56213236, 0.2780568 , 0.91445708, 0.69120586] 等是图像坐标处的值[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)

【讨论】：

好的。似乎 output_dict['detection_boxes'] 包含所有重叠的框，这就是为什么有这么多数组的原因。谢谢！

是什么决定了有多少重叠框？还有为什么会有这么多重叠的框，为什么要传到可视化层去合并？

我知道这是一个老问题，但我认为这可能会对某人有所帮助。如果在visualize_boxes_and_labels_on_image_array 函数输入变量中增加min_score_thresh，则可以限制重叠框的数量。默认情况下，它设置为0.5，例如，对于我的项目，我不得不将其增加到0.8。

标准化的 bbox 格式为 - ymin, xmin, ymax, xmax github.com/tensorflow/models/blob/…

【参考方案2】：

我也有同样的故事。当图像上只显示一个时，得到一个包含大约一百个框 (output_dict['detection_boxes']) 的数组。深入挖掘绘制矩形的代码能够提取并在我的inference.py中使用：

#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])

【参考方案3】：

上面的答案对我不起作用，我不得不做一些改变。所以如果这没有帮助，不妨试试这个。

# 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)

这里的坐标列表中的每一项（字典）都是一个要在图像上绘制的框，带有框坐标（标准化）、class_name 和 score。