python之Tensorflow 对象检测 API : output boxes for probability less than 50%

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) 

我如何更改代码，使其以 > 10% 的概率输出围绕对象的框

请您参考如下方法:

应该很简单。

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是否还有其他可以设置的参数。

其中您可以找到: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sh