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

I'm trying to run the following code on a jupyter notebook:

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, (640, 480)))

if cv2.waitKey(1) & 0xFF == ord('q'):
    cap.release()
    break

And getting the following error:

ValueError Traceback (most recent call last)
in
4
5 input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
----> 6 detections = detect_fn(input_tensor)
7
8 num_detections = int(detections.pop('num_detections'))

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
812 # In this case we have not created variables on the first call. So we can
813 # run the first trace but we should fail if variables are created.
--> 814 results = self._stateful_fn(*args, **kwds)
815 if self._created_variables:
816 raise ValueError("Creating variables on a non-first call to a function"

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\function.py in call(self, *args, **kwargs)
2826 """Calls a graph function specialized to the inputs."""
2827 with self._lock:
-> 2828 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2829 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2830

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args, kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function, args, kwargs

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3073 arg_names=arg_names,
3074 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3075 capture_by_value=self._capture_by_value),
3076 self._function_attributes,
3077 function_spec=self.function_spec,

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984 _, original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args, **func_kwargs)
987
988 # invariant: func_outputs contains only Tensors, CompositeTensors,

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
598 # wrapped allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().wrapped(*args, **kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602

c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise

ValueError: in user code:

<ipython-input-49-d1f74bd6a9c5>:3 detect_fn  *
    image, shapes = detection_model.preprocess(image)
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\object_detection\meta_architectures\ssd_meta_arch.py:484 preprocess  *
    normalized_inputs, self._image_resizer_fn)
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\object_detection\utils\shape_utils.py:492 resize_images_and_return_shapes  *
    outputs = static_or_dynamic_map_fn(
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\object_detection\utils\shape_utils.py:246 static_or_dynamic_map_fn  *
    outputs = [fn(arg) for arg in tf.unstack(elems)]
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\object_detection\core\preprocessor.py:3214 resize_image  *
    new_image = tf.image.resize_images(
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper  **
    return target(*args, **kwargs)
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\ops\image_ops_impl.py:1367 resize_images
    skip_resize_if_same=True)
c:\users\admin\appdata\local\programs\python\python37\lib\site-packages\tensorflow\python\ops\image_ops_impl.py:1219 _resize_images_common
    raise ValueError('\'images\' must have either 3 or 4 dimensions.')

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

can anyone help?