U-NET预测转置掩码

下面是一个使用U-Net模型进行预测和转置掩码的代码示例：

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Dropout, Conv2DTranspose, concatenate

# 定义U-Net模型
def unet_model(input_shape):
    inputs = Input(input_shape)

    # 下采样路径
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(inputs)
    conv1 = Conv2D(64, 3, activation='relu', padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

    conv2 = Conv2D(128, 3, activation='relu', padding='same')(pool1)
    conv2 = Conv2D(128, 3, activation='relu', padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

    conv3 = Conv2D(256, 3, activation='relu', padding='same')(pool2)
    conv3 = Conv2D(256, 3, activation='relu', padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

    conv4 = Conv2D(512, 3, activation='relu', padding='same')(pool3)
    conv4 = Conv2D(512, 3, activation='relu', padding='same')(conv4)
    drop4 = Dropout(0.5)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)

    # 中间层
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(pool4)
    conv5 = Conv2D(1024, 3, activation='relu', padding='same')(conv5)
    drop5 = Dropout(0.5)(conv5)

    # 上采样路径
    up6 = Conv2DTranspose(512, 2, strides=(2, 2), padding='same')(drop5)
    merge6 = concatenate([drop4, up6], axis=3)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(merge6)
    conv6 = Conv2D(512, 3, activation='relu', padding='same')(conv6)

    up7 = Conv2DTranspose(256, 2, strides=(2, 2), padding='same')(conv6)
    merge7 = concatenate([conv3, up7], axis=3)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(merge7)
    conv7 = Conv2D(256, 3, activation='relu', padding='same')(conv7)

    up8 = Conv2DTranspose(128, 2, strides=(2, 2), padding='same')(conv7)
    merge8 = concatenate([conv2, up8], axis=3)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(merge8)
    conv8 = Conv2D(128, 3, activation='relu', padding='same')(conv8)

    up9 = Conv2DTranspose(64, 2, strides=(2, 2), padding='same')(conv8)
    merge9 = concatenate([conv1, up9], axis=3)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(merge9)
    conv9 = Conv2D(64, 3, activation='relu', padding='same')(conv9)
    conv9 = Conv2D(2, 3, activation='relu', padding='same')(conv9)

    # 输出层
    conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)

    model = Model(inputs=inputs, outputs=conv10)
    return model

# 使用U-Net模型进行预测和转置掩码
def predict_and_transpose_mask(image):
    # 加载训练好的模型权重
    model = unet_model(input_shape=(256, 256, 3))
    model.load_weights('unet_weights.h5')

    # 对输入图像进行预测
    predicted_mask = model.predict(image)

    # 转置掩码
    transposed_mask = tf.transpose(predicted_mask, perm