title: Saving the objects detected in a dataframe: tensorflow object_detection
body: <p>I'm running the typical code found in the github repository tensorflow/object_deteciton:
  <a href="https://github.com/tensorflow/models/tree/master/research/object_detection" rel="nofollow noreferrer">https://github.com/tensorflow/models/tree/master/research/object_detection</a></p>
  
  <p>Specifically the 'object_detection_tutorial.ipynb' file. The main loop is this section here:</p>
  
  <pre><code>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)
  </code></pre>
  
  <p>I'm just looking for some advise as to the best way to actually save what the image has identified int a dataframe that would ideally store the category of the object detected for each object detected in the image.</p>
  
  <p>Any help would be appreciate (:</p>