如何在 Tensorflow 对象检测 API 中查找边界框坐标

Posted 2023-03-12

【中文标题】如何在 Tensorflow 对象检测 API 中查找边界框坐标

【英文标题】：How to find bounding boxes coordinates in Tensorflow Object Detection API

【发布时间】：2019-09-30 18:42:42

【问题描述】：

我正在使用 Tensorflow 对象检测 API 代码。我训练了我的模型并获得了很好的检测百分比。我一直在尝试获取边界框坐标，但它不断打印出 100 个奇异数组的列表。

在网上广泛搜索后，我发现数组中的数字是什么意思（边界框坐标是 [0.0, 1.0] 相对于底层图像的宽度和高度的浮点数。）但是，我的数组仍然非常与在线示例中显示的不同。另一个奇怪的事情是，我用少于 100 个图像测试了我的模块，那么怎么会有 100 个边界框坐标的数据。

我得到的数组；

 [[3.13721418e-01 4.65148419e-01 7.11575747e-01 6.85783863e-01]
 [9.78936195e-01 6.50490820e-03 9.97096300e-01 1.82596639e-01]
 [9.51383412e-01 0.00000000e+00 1.00000000e+00 3.88432704e-02]
 [9.85813320e-01 8.96016136e-02 9.97273505e-01 3.15960884e-01]
 [9.88873005e-01 2.13812709e-01 1.00000000e+00 4.14675951e-01]

 ......
 [4.42647263e-02 9.90755498e-01 2.57772505e-01 1.00000000e+00]
 [2.69711018e-05 5.21758199e-02 6.37509704e-01 6.62899792e-01]
 [0.00000000e+00 3.00989419e-01 9.92376506e-02 1.00000000e+00]
 [1.87531322e-01 2.66501214e-04 4.50700432e-01 1.23927500e-02]
 [9.36755657e-01 4.61095899e-01 9.92406607e-01 7.62619019e-01]]

进行检测并获取边界框坐标的函数。 output_dict['detection_boxes'] 是保存上述数组的位置。

def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = output.name for op in ops for output in op.outputs
      tensor_dict = 
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # 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])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[1], image.shape[2])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict=image_tensor: image)

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.int64)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

我希望输出是边界框的常规 x,y 坐标。

【参考方案1】：

output_dict['detection_boxes'] 中的值确实是标准化格式。通过检查您提供的数组中的值，这些值都在 0 和 1 之间，因此它们是合理的。

有 100 个框，因为模型总是输出相同数量的边界框。 （等于配置文件中的max_total_detections）。但并不是所有的都总是有意义的，你需要根据置信度分数过滤掉一些框，它存储在output_dict['scores']中。

获取常规边界框。您可以执行以下操作：

boxes = np.squeeze(output_dict['detection_boxes'])
scores = np.squeeze(output_dict['detection_scores'])
#set a min thresh score, say 0.8
min_score_thresh = 0.8
bboxes = boxes[scores > min_score_thresh]

#get image size
im_width, im_height = image.size
final_box = []
for box in bboxes:
    ymin, xmin, ymax, xmax = box
    final_box.append([xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height])

【讨论】：

如果此解决方案不起作用，请告诉我。