如何修复int（）参数必须是字符串，类似字节的对象或数字，而不是在python中进行视频处理时的'NoneType'？ （物体检测）

Question

我真的是深度学习的新手，请原谅我在该领域缺乏知识。所以基本上我想用视频流测试我训练有素的模型.pb。这是我用来执行此操作的代码。

video = cv2.VideoCapture(PATH_TO_VIDEO)

while(video.isOpened()):

    # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
    # i.e. a single-column array, where each item in the column has the pixel RGB value
    ret, frame = video.read()
    frame_expanded = np.expand_dims(frame, axis=0)

    # Perform the actual detection by running the model with the image as input
    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: frame_expanded})

    # Draw the results of the detection (aka 'visulaize the results')
    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,
        min_score_thresh=0.80)

    # All the results have been drawn on the frame, so it's time to display it.
    cv2.imshow('Object detector', frame)

    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):
        break

# Clean up
video.release()
cv2.destroyAllWindows()

一切正常，我可以运行它，我看到视频流打开了，甚至很好地看到了在检测到的对象上绘制的框。但是，在1-2秒后，程序将停止并退出，并出现以下错误：

np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
  File "C:\Users\User\Anaconda3\lib\site-packages\numpy\core\numeric.py", line 538, in asarray
    return array(a, dtype, copy=False, order=order)
TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

我不知道如何解决这个问题，因为在错误中我什至不了解它在说什么程序，因为我的程序只有100行。任何帮助表示赞赏！提前致谢！保持安全！

Answer 1

添加这些内容

    if ret == False:
         break

video = cv2.VideoCapture(PATH_TO_VIDEO)

while(video.isOpened()):

    # Acquire frame and expand frame dimensions to have shape: [1, None, None, 3]
    # i.e. a single-column array, where each item in the column has the pixel RGB value
    ret, frame = video.read()
    if ret == False:
         break
    frame_expanded = np.expand_dims(frame, axis=0)

    # Perform the actual detection by running the model with the image as input
    (boxes, scores, classes, num) = sess.run(
        [detection_boxes, detection_scores, detection_classes, num_detections],
        feed_dict={image_tensor: frame_expanded})

    # Draw the results of the detection (aka 'visulaize the results')
    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,
        min_score_thresh=0.80)

    # All the results have been drawn on the frame, so it's time to display it.
    cv2.imshow('Object detector', frame)

    # Press 'q' to quit
    if cv2.waitKey(1) == ord('q'):
        break

# Clean up
video.release()
cv2.destroyAllWindows()