Tensorflow 对象检测 API：概率小于 50% 的输出框答案

【问题标题】：Tensorflow object detection API: output boxes for probability less than 50%Tensorflow 对象检测 API：概率小于 50% 的输出框
【发布时间】：2018-08-01 17:09:48
【问题描述】：

我指的是 Tensorflow 对象检测 API (https://github.com/tensorflow/models/tree/master/research/object_detection)：这是我正在使用的检测代码的 IPython 笔记本 (https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb)。在此文件中，输出值设置为绘制概率大于 50% 的框检测代码：

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')

    #myFile = open('example2.csv', 'w')
    i=0
    #boxeslist=[]
    new_boxes = []
    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)

如何更改代码，使其以 > 10% 的概率输出对象周围的框

【问题讨论】：

标签： python tensorflow machine-learning deep-learning object-detection

【解决方案1】：

应该很容易。

如您所见，本教程调用函数“vis_util.visualize_boxes_and_labels_on_image_array”，其参数为：

image
boxes
classes
scores
category_index
use_normalized_coordinates
line_thickness

如果在文件中搜索：'research/object_detection/utilis/visualization_utils.py'，您可以找到该函数并查看您可以设置的其他参数。

您可以在其中找到：min_score_tresh 设置为 .5

如果你设置：

min_score_tresh=.1

应该会得到想要的结果。

小心，因为会被吓到

【讨论】：

【解决方案2】：

简单的方法是在“vis_util.visualize_boxes_and_labels_on_image_array”中添加“min_score_thresh”来设置检测阈值：

      # 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,
          min_score_thresh=.1) # <<======== Add this line for threshold

【讨论】：