Python で画像セマンティック セグメンテーション テクノロジを使用するにはどうすればよいですか?

pip install tensorflow==2.4.0
pip install Pillow

from PIL import Image
im = Image.open('example.jpg')

# 导入预训练的 DeepLab-v3+ 模型
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.layers import Conv2DTranspose, Concatenate, Activation, MaxPooling2D, Conv2D, BatchNormalization, Dropout 

def create_model(num_classes):
    # 加载 MobileNetV2 预训练模型
    base_model = MobileNetV2(input_shape=(256, 256, 3), include_top=False, weights='imagenet')

    # 获取对应层输出的张量
    low_level_features = base_model.get_layer('block_1_expand_relu').output
    x = base_model.get_layer('out_relu').output

    # 通过使用反卷积尺寸进行上采样和空洞卷积，构建 DeepLab-v3+ 系统，并针对特定的数据集来训练其分类器
    x = Conv2D(256, (1, 1), activation='relu', padding='same', name='concat_projection')(x)
    x = Dropout(0.3)(x)
    x = Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same', name='decoder_conv0')(x)
    x = BatchNormalization(name='decoder_bn0')(x)
    x = Activation('relu', name='decoder_relu0')(x)
    x = Concatenate(name='decoder_concat0')([x, low_level_features])
    x = Conv2D(128, (1, 1), padding='same', name='decoder_conv1')(x)
    x = Dropout(0.3)(x)
    x = Conv2DTranspose(64, (3, 3), strides=(2, 2), padding='same', name='decoder_conv2')(x)
    x = BatchNormalization(name='decoder_bn2')(x)
    x = Activation('relu', name='decoder_relu2')(x)
    x = Conv2D(num_classes, (1, 1), padding='same', name='decoder_conv3')(x)
    x = Activation('softmax', name='softmax')(x)

    # 创建 Keras 模型，并返回它
    model = Model(inputs=base_model.input, outputs=x)

    return model

import numpy as np
import urllib.request

# 读取图像
urllib.request.urlretrieve('https://www.tensorflow.org/images/surf.jpg', 'image.jpg')
image = Image.open('image.jpg')
image_array = np.array(image)

# 加载训练好的模型
model = create_model(num_classes=21)
model.load_weights('deeplabv3_xception_tf_dim_ordering_tf_kernels.h5')
print('模型加载成功。')

# 将输入图像调整为模型所需形状，并进行语义分割
input_tensor = tf.convert_to_tensor(np.expand_dims(image_array, 0))
output_tensor = model(input_tensor)

# 显示语义分割结果
import matplotlib.pyplot as plt

parsed_results = output_tensor.numpy().squeeze()
parsed_results = np.argmax(parsed_results, axis=2)
plt.imshow(parsed_results)
plt.show()