Question

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

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，以便在对象周围输出＆gt;概率为10％

Answer 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'，您可以找到该功能，并看到您可以设置其他参数。

其中包括：min_score_tresh设置为.5

如果你设置：

min_score_tresh=.1

应该获得所需的结果。

小心，原因是sh

Answer 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

Tensorflow对象检测API：概率小于50％的输出框

2 个答案: