Question

我正在使用Tensorflow对象检测API代码。我训练了模型，并获得了很高的检测率。我一直在尝试获取边界框的坐标，但它一直在打印出100个奇异数组的列表。

在网上进行了广泛搜索之后，我发现了数组中的数字是什么意思（边界框坐标是相对于基础图像的宽度和高度的[0.0，1.0]浮点数。）但是，我的数组仍然非常与在线示例中显示的有所不同。另一个怪异的事情是，我用不到100张图像测试了我的模块，因此，如何甚至有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]]

执行检测并获取边界框坐标的函数。 output_dict ['detection_boxes']是保存上面的数组的位置。

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

我希望输出结果是边界框的常规x，y坐标。

Answer 1

def read(text_file): data, i = {}, 0 with open(text_file) as f: for line in f: i = i + 1 data['row_%d'%i] = line.rstrip('\n') return data res = {} for i, fname in enumerate([r'File1.txt', r'File2.txt']): res[i] = read(fname) with open(out_file, 'w') as f: json.dump(res, f)中的值确实采用规范化格式。通过检查您提供的数组中的值，这些值都在0到1之间，因此它们是合理的。

有100个框，因为模型始终输出相同数量的边界框。（它等于配置文件中的output_dict['detection_boxes']）。但是并非所有这些都总是有意义的，您需要根据存储在max_total_detections中的置信度分数过滤掉一些框。

获取常规边界框。您可以执行以下操作：

output_dict['scores']

如何在Tensorflow Object Detection API中查找边界框坐标

1 个答案: