Question

我正在使用TensorFlow的ObjectDetection部分运行默认的iPython笔记本： https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

我可以使用下面的代码在笔记本的最后一个单元格中打印模型所做注释的坐标。

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    # Definite input and output Tensors for detection_graph
    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
    # Each box represents a part of the image where a particular object was detected.
    detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
    # Each score represent how level of confidence for each of the objects.
    # Score is shown on the result image, together with the class label.
    detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
    detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
    num_detections = detection_graph.get_tensor_by_name('num_detections:0')
    for image_path in TEST_IMAGE_PATHS:
      image = Image.open(image_path)
      # the array based representation of the image will be used later in order to prepare the
      # result image with boxes and labels on it.
      image_np = load_image_into_numpy_array(image)
      # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
      image_np_expanded = np.expand_dims(image_np, axis=0)
      # Actual detection.
      (boxes, scores, classes, num) = sess.run(
          [detection_boxes, detection_scores, detection_classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)
      s_boxes = boxes[scores > 0.5]
      height = 636
      width = 1024
      s_boxes[:,0] = s_boxes[:,0]*height
      s_boxes[:,2] = s_boxes[:,2]*height
      s_boxes[:,1] = s_boxes[:,1]*width
      s_boxes[:,3] = s_boxes[:,3]*width
      for s in s_boxes:
            print(s)
      break

我得到的输出：

我正在尝试打印与模型所做注释相关联的类因此输出应该类似于以下内容（给定＆＃39; Dog＆＃39;在＆＃39; category_index＆＃39;中有索引1）：

[  23.5806942    23.79684448  548.24536133  326.084198  ]: 1
[  63.68989563  401.32214355  609.81091309  996.93786621]: 1

OR

[  23.5806942    23.79684448  548.24536133  326.084198  ]: Dog
[  63.68989563  401.32214355  609.81091309  996.93786621]: Dog

我遇到的主要问题是，我无法弄清楚如何从＆＃39;类＆＃39;中索引元素？对应score > 0.5。

visualize_boxes_and_labels_on_image_array功能在这里：

https://github.com/tensorflow/models/blob/master/research/object_detection/utils/visualization_utils.py#L323

Answer 1

classes的索引类似于boxes

s_class = classes[scores > 0.5]
print(s_class)

对于对象检测iPynb中的第一个示例，将返回[ 18. 18.]。 18对应category_index

中的狗

与检测到的对象关联的打印类

1 个答案: