TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

import os
import pathlib
import matplotlib
import matplotlib.pyplot as plt
import io
import scipy.misc
import numpy as np
from six import BytesIO
from PIL import Image, ImageDraw, ImageFont
from six.moves.urllib.request import urlopen
import tensorflow as tf
import tensorflow_hub as hub
tf.get_logger().setLevel('ERROR')

git clone --depth 1 https://github.com/tensorflow/models 

protoc object_detection/protos/*.proto --python_out=.

..\Anaconda3\envs\tfcpu2.10_py38\Scripts\pip.exe install . -i https://pypi.tuna.tsinghua.edu.cn/simple 

https://tfhub.dev/tensorflow/centernet/hourglass\_512x512\_kpts/1 

https://storage.googleapis.com/tfhub-modules/tensorflow/centernet/hourglass\_512x512\_kpts/1.tar.gz 

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils
from object_detection.utils import ops as utils_ops
PATH_TO_LABELS = './models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
model_path = 'https://storage.googleapis.com/tfhub-modules/tensorflow/centernet/hourglass_512x512_kpts/1.tar.gz'
print('TensorFlow Hub 中的模型地址: {}'.format(model_path))
print('加載模型...')
hub_model = hub.load(model_path)
print('加載成功！')

TensorFlow Hub 中的模型地址: https://storage.googleapis.com/tfhub-modules/tensorflow/centernet/hourglass_512x512_kpts/1.tar.gz
加載模型...
WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_42408) with ops with custom gradients. Will likely fail if a gradient is requested.
WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_209416) with ops with custom gradients. Will likely fail if a gradient is requested.
...
WARNING:absl:Importing a function (__inference_batchnorm_layer_call_and_return_conditional_losses_56488) with ops with custom gradients. Will likely fail if a gradient is requested.
加載成功！

def load_image_into_numpy_array(path):
    image = None
    if(path.startswith('http')):
        response = urlopen(path)
        image_data = response.read()
        image_data = BytesIO(image_data)
        image = Image.open(image_data)
    else:
        image_data = tf.io.gfile.GFile(path, 'rb').read()
        image = Image.open(BytesIO(image_data))
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape((1, im_height, im_width, 3)).astype(np.uint8)
IMAGES_FOR_TEST = {
  'Beach' : 'models/research/object_detection/test_images/image2.jpg',
  'Dogs' : 'models/research/object_detection/test_images/image1.jpg',
  'Naxos Taverna' : 'https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg',
  'Beatles' : 'https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_%28Plate_125%29_%288592917784%29.jpg',
  'Phones' : 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg/1024px-Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg',
  'Birds' : 'https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.jpg',
}
COCO17_HUMAN_POSE_KEYPOINTS = [(0, 1), (0, 2),(1, 3),(2, 4),(0, 5),(0, 6),(5, 7),(7, 9),(6, 8),(8, 10),(5, 6),(5, 11), (6, 12),(11, 12),(11, 13),(13, 15),(12, 14),(14, 16)]
%matplotlib inline
selected_image = 'Dogs' 
image_path = IMAGES_FOR_TEST[selected_image]
image_np = load_image_into_numpy_array(image_path)
plt.figure(figsize=(24,32))
plt.imshow(image_np[0])
plt.show()

results = hub_model(image_np)
result = {key:value.numpy() for key,value in results.items()}
label_id_offset = 0
image_np_with_detections = image_np.copy()
keypoints, keypoint_scores = None, None
if 'detection_keypoints' in result:
    keypoints = result['detection_keypoints'][0]
    keypoint_scores = result['detection_keypoint_scores'][0]
viz_utils.visualize_boxes_and_labels_on_image_array(
      image_np_with_detections[0],
      result['detection_boxes'][0],
      (result['detection_classes'][0] + label_id_offset).astype(int),
      result['detection_scores'][0],
      category_index,
      use_normalized_coordinates=True,
      max_boxes_to_draw=200,
      min_score_thresh=.30,
      agnostic_mode=False,
      keypoints=keypoints,
      keypoint_scores=keypoint_scores,
      keypoint_edges=COCO17_HUMAN_POSE_KEYPOINTS)
plt.figure(figsize=(24,32))
plt.imshow(image_np_with_detections[0])
plt.show()