Tensorflow对象检测API：将检测到的类打印为输出到终端

Question

我有一个简单的问题，但我无法弄清楚如何去做。我正在使用TF Object检测API来检测图像，它工作正常，并给出了一个图像，它将绘制边界框，并给出它认为检测到的类别的标签和置信度分数。我的问题是，如何将检测到的类（作为字符串）打印到终端，即不仅在图像上，而且还作为输出到终端。

下面是负责图像检测

with detection_graph.as_default(): 
with tf.Session(graph=detection_graph) as sess: 
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) 
    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. 
    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. 
    scores = detection_graph.get_tensor_by_name('detection_scores:0') 
    classes = detection_graph.get_tensor_by_name('detection_classes:0') 
    num_detections = detection_graph.get_tensor_by_name('num_detections:0') 
    # Actual detection. 
    (boxes, scores, classes, num_detections) = sess.run(
     [boxes, scores, 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, min_score_thresh=.2) 
    plt.figure(figsize=IMAGE_SIZE) 
    plt.imshow(image_np) 
    plt.show() 

在此先感谢代码，堆栈溢出的第一篇文章，所以请去容易对我

Answer 1

嗯，这是很容易的。该classes被加密的category_index这是一个dict，所以你可以做这样的事情：

with detection_graph.as_default(): 
with tf.Session(graph=detection_graph) as sess: 
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) 
    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. 
    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. 
    scores = detection_graph.get_tensor_by_name('detection_scores:0') 
    classes = detection_graph.get_tensor_by_name('detection_classes:0') 
    num_detections = detection_graph.get_tensor_by_name('num_detections:0') 
    # Actual detection. 
    (boxes, scores, classes, num_detections) = sess.run(
     [boxes, scores, classes, num_detections], 
     feed_dict={image_tensor: image_np_expanded}) 

    # Here output the category as string and score to terminal 
    print([category_index.get(i) for i in classes[0]]) 
    print(scores) 

Answer 2

DAT和Omar..I有一个基本的问题。当我们打印的阵列，它包含数组排名前100的分数和分类。其中只有2到3个实际显示在输出图像中（带有有界的框和精度）。我怎样才能只对那些实际显示在输出图像中的值进行分类？是否可能或者我们是否需要设置一个固定的准确度阈值？ （并且可能会丢失输出图像中显示的某些对象）。

Answer 3

只需转到object_detection文件夹中的utils目录并打开脚本visualization_utils.py。你会发现一个函数，即visualize_boxes_and_labels_on_image_array，在函数的结尾添加一个打印命令来打印变量class_name（print（class_name））。现在运行你的代码并看到魔法。