Question

我正在使用TF API for object detection来检测视频流中的对象。问题是即使使用移动模型，每帧检测对象也会降低视频速度。所以我想知道我是否可以检测到一次该对象并继续跟踪它的剩余时间。但不幸的是，我无法弄清楚如何做到这一点。我尝试过opencv跟踪API，但在整个时间段内跟踪同一个对象时遇到了问题。如果有人可以指导我，那将会有很大的帮助。

谢谢！

我的工作代码：

# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

sess = tf.Session(graph = detection_graph)
cap = cv2.VideoCapture("someVide.mp4")
while(cap.isOpened()):
     ret, frame = cap.read()
     if ret == True:
          frame_expanded = np.expand_dims(frame, axis=0)
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        [boxes, scores, classes, num_detections] = [detection_graph.get_tensor_by_name('detection_boxes:0'), \
        detection_graph.get_tensor_by_name('detection_scores:0'), detection_graph.get_tensor_by_name('detection_classes:0'), \
        detection_graph.get_tensor_by_name('num_detections:0')]
        (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections], \
            feed_dict = {image_tensor: frame_expanded})

        b = vis_util.visualize_boxes_and_labels_on_image_array(
            frame,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates=True,
            line_thickness=8)
   else:
      break

图像上的对象跟踪

0 个答案: