问ValueError:图像必须具有3个维度或4个维度。在Colab

EN

ValueError: in user code:

    <ipython-input-49-1e7efe9130ee>:11 detect_fn  *
        image, shapes = detection_model.preprocess(image)
    /usr/local/lib/python3.7/dist-packages/object_detection/meta_architectures/ssd_meta_arch.py:484 preprocess  *
        normalized_inputs, self._image_resizer_fn)
    /usr/local/lib/python3.7/dist-packages/object_detection/utils/shape_utils.py:492 resize_images_and_return_shapes  *
        outputs = static_or_dynamic_map_fn(
    /usr/local/lib/python3.7/dist-packages/object_detection/utils/shape_utils.py:246 static_or_dynamic_map_fn  *
        outputs = [fn(arg) for arg in tf.unstack(elems)]
    /usr/local/lib/python3.7/dist-packages/object_detection/core/preprocessor.py:3241 resize_image  *
        new_image = tf.image.resize_images(
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper  **
        return target(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/image_ops_impl.py:1468 resize_images
        skip_resize_if_same=True)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/image_ops_impl.py:1320 _resize_images_common
        raise ValueError('\'images\' must have either 3 or 4 dimensions.')

    ValueError: 'images' must have either 3 or 4 dimensions.

while True: 
    ret, frame = cap.read()
    image_np = np.array(frame)
    
    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
    detections = detect_fn(input_tensor)
    
    num_detections = int(detections.pop('num_detections'))
    detections = {key: value[0, :num_detections].numpy()
                  for key, value in detections.items()}
    detections['num_detections'] = num_detections

    # detection_classes should be ints.
    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

    label_id_offset = 1
    image_np_with_detections = image_np.copy()

    viz_utils.visualize_boxes_and_labels_on_image_array(
                image_np_with_detections,
                detections['detection_boxes'],
                detections['detection_classes']+label_id_offset,
                detections['detection_scores'],
                category_index,
                use_normalized_coordinates=True,
                max_boxes_to_draw=5,
                min_score_thresh=.5,
                agnostic_mode=False)

    cv2.imshow('object detection',  cv2.resize(image_np_with_detections, (800, 600)))
    
    if cv2.waitKey(1) & 0xFF == ord('q'):
        cap.release()
        break