用py编程代码示例实现， 语义分割任务 编程完成一个基于深度学习的图象语义分割任务的训练，完成预测、评估等。选取至少10张图片，输出预测结果，并分析结果。 需要一个具体的代码示例

在Python中，我们通常会使用深度学习库如TensorFlow或PyTorch来实现图像语义分割任务。这里我将给出一个使用Keras和U-Net模型的基本例子，因为U-Net是一个常用于语义分割的经典的卷积神经网络架构。

首先，确保已经安装了以下库：

pip install tensorflow keras_segmentation scikit-image

以下是简单的U-Net模型训练和预测的代码示例：

import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Input, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from skimage.io import imread, imshow, concatenate_images
from skimage.transform import resize
from sklearn.model_selection import train_test_split
from keras_segmentation.utils.train_utils import get_batches

# 加载数据集（假设你已经有了训练和验证图像及对应的标签）
train_image_dir = 'path/to/train/images'
train_mask_dir = 'path/to/train/masks'
val_image_dir = 'path/to/validation/images'
val_mask_dir = 'path/to/validation/masks'

# 加载并预处理图像和标签
def load_data(image_dir, mask_dir):
    images = []
    masks = []
    for img_name in os.listdir(image_dir):
        img_path = os.path.join(image_dir, img_name)
        mask_path = os.path.join(mask_dir, img_name.replace('.jpg', '.png'))
        image = imread(img_path)
        mask = imread(mask_path)
        # 将图像和mask调整到相同的尺寸（例如256x256）
        image, mask = resize((image, mask), (256, 256))
        images.append(image)
        masks.append(mask)
    return np.array(images), np.array(masks)

# 准备训练数据
X_train, y_train = load_data(train_image_dir, train_mask_dir)
X_val, y_val = load_data(val_image_dir, val_mask_dir)

# 定义U-Net模型
input_shape = X_train.shape[1:]
inputs = Input(input_shape)
down_stack = [
    Downsample(32, 3, activation='relu'),  # 第一层
    Downsample(64, 3, activation='relu'),  # 第二层
    Downsample(128, 3, activation='relu'),  # 第三层
    Downsample(256, 3, activation='relu'),  # 第四层
]
up_stack = [
    Upsample(128, 2, activation='relu'),  # 第五层
    Upsample(64, 2, activation='relu'),  # 第六层
    Upsample(32, 2, activation='relu'),  # 第七层
    Conv2D(1, 1, activation='sigmoid')  # 输出层
]

# 构建模型
x = inputs
skip_connections = []

for down in down_stack:
    x = down(x)
    skip_connections.append(x)

skip_connections = reversed(skip_connections[:-1])

for up, skip in zip(up_stack, skip_connections):
    x = up(x)
    x = concatenate([x, skip], axis=-1)

outputs = x

model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=Adam(lr=1e-4), loss=tf.keras.losses.BinaryCrossentropy(), metrics=['accuracy'])

# 训练模型
epochs = 10
batch_size = 8
model.fit(
    get_batches(X_train, y_train, batch_size),
    validation_data=get_batches(X_val, y_val, batch_size),
    epochs=epochs,
    verbose=1
)

# 预测
test_image_dir = 'path/to/test/images'
test_image_names = os.listdir(test_image_dir)
predictions = []
for img_name in test_image_names:
    img_path = os.path.join(test_image_dir, img_name)
    img = imread(img_path)
    img = resize(img, (256, 256))
    pred = model.predict(np.expand_dims(img, axis=0))
    predictions.append(pred[0])

# 分析结果
# 可能需要额外的库如matplotlib展示每个预测的分割结果
imshow(combined_image)
imshow(prediction, alpha=0.5)
plt.show()